Online Calculator Research Articles

Importance of Glomerular Filtration Rate Determination and its Role in Drug-Dose Adjustments with Special Reference to Oncology

Glomerular filtration Rate (GFR) measured in steady state is the best marker of kidney function. Glomerular filtration rate can be determined using exogenous markers like inulin, iothalamate, or iohexol (mGFR), or estimated using endogenous markers like creatinine and cystatin C (eGFR). Several equations are available, CKD-EPI equation is the recommended equation as per current guidelines. CKD-EPI creatinine equation refit (without the race variable) – 2021, and CKD-EPI creatinine - cystatin C equation refit (without the race variable)-2021 are the most recent recommendations from the National Kidney Foundation and the American Society of Nephrology. Dose modifications of many drugs are needed in kidney dysfunction. In chronic kidney disease, as the kidney function is stable, determination of GFR is more accurate and can be used to modify the drug doses. Since the kidney function is not stable in acute kidney injury, determination of GFR is more difficult and it is not accurate. This makes it difficult to use it for determining dose modifications of drugs. Therapeutic drug level monitoring, and adjusting the dose based on pharmacokinetics/pharmacodynamics knowledge of the drug could be helpful in such cases. In oncology, accurate GFR measurement is crucial for adjusting the doses of chemotherapeutic agents to avoid toxicity and ensure efficacy, as cancer patients often have compromised kidney function due to the disease or its treatment. In this manuscript, we review all these aspects to provide a comprehensive understanding of GFR measurement and its implications in drug-dose adjustments, with a particular focus on how accurate GFR assessment is essential for optimizing chemotherapy dosing in oncology patients to minimize toxicity and maximize therapeutic efficacy. Frequently, medical professionals need to depend on online calculators to get the value of eGFR, as these equations can be complicated. Scientific calculators can be used too, but that is not a common practice in nephrology clinics. The authors have shown in this paper how to use Microsoft Excel to create personal calculators, so that eGFR may be calculated by medical professionals and dialysis staff even when internet is not available due to institutional policies or otherwise. The ‘Appendix’ section given at the end of the paper shows how to create these personal calculators in Microsoft excel. Calculators for CKD-EPI equations can also be developed in excel, however the calculations are more complex, so I prefer using the programming language python to develop these calculators. In this paper, the authors have shown only the excel based calculators for CG equation and MDRD-4 (re-expressed) equation.

Echocardiographic predictors of surgical outcomes in pulmonary atresia with intact ventricular septum and development of a discriminatory calculator: an Australian tertiary institution experience.

Pulmonary atresia with intact ventricular septum is a rare congenital cardiac lesion with significant anatomical heterogeneity. Surgical planning of borderline cases remains challenging and is primarily based on echocardiography. The aim was to identify echocardiographic parameters that correlate with surgical outcome and to develop a discriminatory calculator. Retrospective review of all pulmonary atresia with intact ventricular septum cases at a statewide tertiary paediatric cardiac centre was performed between 2004 and 2020. Demographic, clinical, and echocardiographic data were collected. Logistic regression was used to develop a discriminatory tool for prediction of biventricular repair. Forty patients were included. Overall mortality was 27.5% (n = 11) and confined to patients managed as univentricular (11 vs 0, p = 0.027). Patients who underwent univentricular palliation were more likely to have an associated coronary artery abnormality (17 vs 3, p = 0.001). Fifteen surviving patients (51.7%) achieved biventricular circulation while 14 (48.3%) required one-and-a-half or univentricular palliation. Nineteen patients (47.5%) underwent percutaneous pulmonary valve perforation. No patients without tricuspid regurgitation achieved biventricular repair. The combination of tricuspid valve/mitral valve annulus dimension ratio and right ventricle/left ventricle length ratio identified biventricular management with a sensitivity of 93% and specificity of 96%. An online calculator has been made available. Pulmonary atresia with intact ventricular septum is a challenging condition with significant early and interstage morbidity and mortality risk. Patient outcomes were comparable to internationally reported data. Right ventricle/left ventricle length and tricuspid valve/mitral valve annulus dimension ratios identified a biventricular pathway with a high level of sensitivity and specificity. Absent tricuspid regurgitation was associated with a univentricular outcome.

Comparison of two strategies estimating surgically induced astigmatism in position prediction of toric IOLs in 2.2mm cataract surgery.

To compare the precision of the arithmetic mean of surgically induced astigmatism (M-SIA) and the centroid of surgically induced astigmatism (C-SIA) in estimating SIA when predicting the power and axis of toric IOLs under different circumstances. 120 eyes of 99 patients undergoing toric IOL replacement in a simple cataract surgery were included in the retrospective study. The predicted position of toric IOL was calculated by Z Calc online calculator and Barrett Toric Calculator with M-SIA (0.4D) or C-SIA (0.1D). Position prediction error (PPE, the difference between the predicted position of toric IOL and the ideal position of toric IOL), its absolute value (PPE-Abs) and △PPE (the difference between absolute value of PPE calculated by C-SIA and by M-SIA using the same toric calculator) were used to evaluate the precision in estimate of SIA. Statistical significance in mean PPE and mean PPE-Abs was found in the group M-Barrett. The orientation of corneal astigmatism and eye laterality were influential to mean PPE while gender was not. △PPE was below 0 in both toric calculators. Significant difference in △PPE was seen between groups with different corneal astigmatism when all eyes were OS, as well as between groups with different eye laterality when all eyes were with WTR astigmatism. We did not find a significant difference between M-SIA and C-SIA in estimating the SIA of the patients undergoing small-incision cataract surgery combined with toric IOL implantation. M-SIA was not recommended in Barrett Toric Calculator. The orientation of corneal astigmatism and the incision on the cornea should be considered when choosing between M-SIA and C-SIA. WHAT IS KNOWN? : Either the arithmetic mean of surgically induced astigmatism (M-SIA) or the centroid of surgically induced astigmatism (C-SIA) of a cohort was used to estimate the position of toric IOLs before the surgery. The size and site of corneal incision could influence the orientation and the magnitude of SIA. WHAT IS NEW? : When calculating the axis of toric IOLs with Barrett Toric Calculator, arithmetic mean of SIA (M-SIA) was not recommended for estimate. The orientation of corneal astigmatism could influence the estimate of SIA. We created several novel variables that could be used to indirectly evaluate the stability of toric IOLs and the prognosis of patients.

Magnets in Conservation of Easel Paintings: Mechanical Forces between Magnets and a Thin Paint Layer

ABSTRACT This paper discusses the mechanical interactions between a magnet and a thin paint layer situated on a non-magnetic substrate, for example, to correct painting deformations or to fix pictorial layers. Crucial to calculating the attraction force F is knowing the magnetic susceptibility χ in the paint layer center XC and also the gradient dH/dx across the paint (calculated from magnetic fields H on both sides XS and XE of the layer). However, the susceptibility χ depends on the magnetic field H which is a function of the distance from the magnet’s pole: χ = f(H(XC)). It should be noted that as the magnet approaches the paint layer, the value of the field H(x) increases, but the susceptibility of the substance χ(H(x)) decreases. Analytical formulas and practical calculation schemes for mechanical forces in the discussed magnetic systems are presented in detail. As a result of the undertaken work, interactive on-line calculators for calculating magnetic fields and forces were developed and made available online. Reliable estimation of mechanical forces due to magnets, as presented here, allows for more controlled and reproducible conservation results. Therefore, the principle of minimal intervention and reversibility can be attained repeatably with greater ease, laying the groundwork for improved protocols.

Multi-Center Real-World Data Driven Web Calculator for Predicting Outcomes in IDH-Mutant Gliomas: Integrating Molecular Subtypes and Treatment Modalities

Abstract Background IDH-mutant gliomas generally have a better prognosis than IDH-wild-type glioblastomas, and the extent of resection significantly impacts prognosis. However, there is a lack of integrated tools for predicting outcomes based on molecular subtypes and treatment modalities. This study aimed to identify factors influencing gross total resection (GTR) rates and to develop a clinical prognostic tool for IDH-mutant gliomas. Methods We analyzed 650 patients with IDH-mutant gliomas from three Chinese medical centers (Shanghai, Hong Kong, and Zhengzhou). Data included age, sex, extent of resection, radiotherapy status, tumor grade, histology, and molecular markers (1p19q, TERT promoter, BRAF, EGFR, 10q). Patients were categorized based on GTR status, and a nomogram predicting 3-, 5-, and 10-year overall survival (OS) was developed using Cox proportional hazards regression and validated with time-dependent ROC and calibration plot analyses. Results Non-GTR was associated with diffuse astrocytoma (73.0% vs. 53.5%), 1p19q non-codeletion (67.9% vs. 48.7%), and wildtype TERT promoter (63.6% vs. 52.4%). The nomogram, incorporating age, TERT promoter status, extent of resection, grade, and radiotherapy status, demonstrated strong discriminatory ability (AUC >0.75) and good calibration. Decision curve analysis indicated that it outperformed WHO grade-based classification in identifying high-risk patients. An online calculator was developed for clinical use (http://www.szflab.site/nomogram/). Conclusion We developed and validated a nomogram and online tool that integrates molecular and clinical factors for predicting outcomes in IDH-mutant gliomas, enhancing clinical decision-making.

Improving the Accuracy of Lens Formulas for in-the-bag Intraocular Lens Implantation in Marfan Syndrome Patients with Ectopia Lentis.

To improve the accuracy of intraocular lens (IOL) power calculation formulas by modifying the effective lens position (ELP) equations for patients with Marfan Syndrome (MFS) and ectopia lentis (EL) undergoing in-the-bag IOL implantation. Eye and ENT Hospital of Fudan University. Retrospective cohort study. The formula-specific ELP was obtained from the SRK/T, T2, Holladay I, and HofferQ formulas. The back-calculated ELP was obtained based on the vergence formula using preoperative biometry, postoperative refraction, and the IOL power. The Generalized Linear Models or Gradient Boosting Machines were used to predict ELP or ELP error. A total of 255 patients (255 eyes) were assigned randomly into a training set and a validation set (7:3 ratio). Linear correlation identified axial length(AL), corneal height, and white-to-white as predictors for ELP and ELP error for patients with shorter AL (AL ≤ 24 mm). For those with longer AL (AL > 24 mm), AL and the central corneal radius were identified as the primary predictors. Incorporating these predictors into the modified ELP formula significantly improved the accuracy in the validation set, including SRK/T, T2, Haigis, Holladay I, and HofferQ formulas. The improvement was more pronounced in patients with shorter AL. Additionally, the GLM-modified formulas outperformed both the Barrett Universal II and Kane formulas. The accuracy across different ocular dimensions was comparable among the modified formulas, based on which an online calculator was developed. Using the more accurately predicted ELP can significantly improve the accuracy of existing formulas in patients with MFS.

Integration of Multiple Climate Change Mitigation Actions and Health Co-Benefits: A Framework Using the Global Calculator.

The Global Calculator is an open-source model of the world's energy, land, and food systems. It is a pioneering online calculator to project the impact of interventions to mitigate climate change on global temperature. A few studies have been conducted to evaluate the health co-benefits of climate change mitigation, though they are still fragmentary. Our objectives are to identify which sectors could yield the greatest results in terms of climate change mitigation and suggest whether existing evidence could be used to weight mitigation actions based on their ancillary impacts on human health or health co-benefits. Using the International Energy Agency (IEA) 4DS scenario as a referent (i.e., the "4-degree Celsius increase scenario"), we simulated changes in different policy "levers" (encompassing 43 potential technological and behavioral interventions, grouped by 14 sectors) and assessed the relative importance of each lever in terms of changes in annual greenhouse gas emissions in 2050 and cumulative emissions by 2100. In addition, we examined existing estimates for the health co-benefits associated with different interventions, using evidence from the Lancet Pathfinder and four other tools. Our simulations suggest that-after accounting for demographic change-transition from fossil fuels to renewables and changes in agriculture, forestry, land use, and food production are key sectors for climate change mitigation. The role of interventions in other sectors, like carbon capture and storage (CCS) or nuclear power, is more modest. Our work also identifies mitigation actions that are likely to have large health co-benefits, including shifts to renewable energy and changes in land use as well as dietary and travel behaviors. In conclusion, some of the sectors/interventions which have been at the center of policy debate (e.g., CCS or nuclear power) are likely to be far less important than changes in areas such as dietary habits or forestry practices by 2050. https://doi.org/10.1289/EHP14906.

Machine-Learning-Based Predictive Model for Bothersome Stress Urinary Incontinence Among Parous Women in Southeastern China.

Accurate identification of female populations at high risk for urinary incontinence (UI) and early intervention are potentially effective initiatives to reduce the prevalence of UI. We aimed to apply machine-learning techniques to establish, internally validate, and provide interpretable risk assessment tools. Data from a cross-sectional epidemiological survey of female urinary incontinence conducted in 2022 were used. Sociodemographic and obstetrics-related characteristics, comorbidities, and urinary incontinence questionnaire results were used to develop multiple prediction models. Seventy percent of the individuals in the study cohort were employed in model training, and the remainder were used for internal validation. Model performance was characterized by area under the receiver-operating characteristic curve (AUC) and calibration curves, as well as Brier scores. The best-performing model was finally selected to develop an online prediction tool. The results showed that bothersome stress urinary incontinence (BSUI) occurred in 9.6% (849 out of 8,830) of parous women. The XGBoost model achieved the best prediction performance (training set: AUC 0.796, 95% confidence interval [CI]: 0.778-0.815, validation set: AUC 0.720, 95% CI: 0.686-0.754). Additionally, the XGBoost model achieved the lowest (best) Brier score among the models, with sensitivity of 0.657, specificity of 0.690, accuracy of 0.688, positive predictive value of 0.231, and negative predictive value of 0.948. Based on this model, the top five risk factors for the development of BSUI among parous women were ranked as follows: body mass index, age, vaginal delivery, constipation, and maximum fetal birth weight. An online calculator was provided for clinical use. The application of machine-learning algorithms provides an acceptable, though not perfect, prediction of BSUI risk among parous women, requiring further validation and improvement in future research.

Incidence, risk factors, outcomes, and prediction model of surgical site infection after hepatectomy for hepatocellular carcinoma: A multicenter cohort study

Background & aimsSurgical site infection (SSI) is a common complication after hepatectomy for hepatocellular carcinoma (HCC), but its risk factors and impact on outcomes remain poorly characterized. We aimed to investigate the incidence, risk factors, and outcomes of SSI after hepatectomy for HCC and develop a prediction model. MethodsThis multicenter retrospective study included patients who underwent curative-intent hepatectomy for HCC across 15 Chinese hepatobiliary centers from 2010 to 2021. SSI was defined according to Centers for Disease Control and Prevention criteria. Logistic regression identified independent risk factors for SSI. A nomogram was developed and internally validated. ResultsAmong 4124 patients, 393 (9.5 %) developed SSI; 76 (19.3 %) were diagnosed after discharge. SSI was associated with prolonged hospital stay (mean: 21 vs. 11 days), higher 30-day readmission (13.7 % vs. 3.2 %), and 90-day mortality (5.1 % vs. 2.9 %) (all P < 0.001). Independent risk factors for SSI were obesity (odds ratio [OR] 2.12), diabetes (OR 3.31), portal hypertension (OR 1.96), blood loss ≥400 mL (OR 1.75), open approach (OR 4.99), diaphragmatic incision (OR 2.27), major hepatectomy (OR 1.88), and operative time ≥180 min (OR 1.55). The nomogram model and online calculator (http://asapcalculate.top/Cal14_en.html) demonstrated good discrimination (C-index 0.733) and calibration. ConclusionsSSI was common after hepatectomy for HCC and associated with worse short-term postoperative outcomes. The novel easy-to-use prediction calculator may facilitate individualized risk assessment and guide targeted preventive strategies. Future studies should focus on external validation and evaluating interventions in high-risk patients.

(Invited) Digital Twin for Inverse Design of Battery Manufacturing Processes

The manufacturing process of lithium ion battery (LIB) electrodes impacts their architecture and the practical properties of the cells, such as their energy and power densities, their durability and safety. Therefore, it is crucial to optimize this manufacturing process in a proper manner. Such an optimization is highly complex because of the very significant amount of parameters and numerous interdependencies between the process steps, such as the slurry mixing, the casting, the drying, the calendering, the assembly, the electrolyte filling and the solid electrolyte interphase formation. As a consequence, traditional trial and error approaches can lead to high scrap rates in LIB cell manufacturing.ARTISTIC is a digital twin for inverse design of LIB manufacturing process that we keep developing since several years.[1] ARTISTIC is supported on a combination of physics-based models and machine learning. The physics-based models simulate the dynamics, with 3D resolution, of the slurry mixing, the slurry coating and its drying, the resulting electrode calendering, the cell infiltration by the liquid electrolyte, the formation step and the resulting electrochemical performance of the virtually produced electrodes and cells. These physics-based models are linked with each other in a sequential manner, and they are supported on a combination of methods, such as Coarse Grained Particle Dynamics, Discrete Element Method, Lattice Boltzmann Method and Finite Element Method. Deep learning is used to derive time-dependent 3D surrogate models mimicking the behavior of the physics-based models with much less computational cost. Furthermore, a wide diversity of machine learning techniques are used to accelerate the calibration of the physics-based models with experimental observables (e.g. slurry viscosity and density, electrode porosity, tortuosity factor and conductivity) and to unravel correlations between manufacturing parameters and electrode and cell properties. All these pieces are integrated in a single computational infrastructure performing the inverse design. For that purpose, the deep learning surrogates and the machine learning models are coupled to a multi-objective Bayesian Optimizer. The latter allows predicting which manufacturing parameters to adopt (e.g. slurry formulation, drying rate, calendering degree) in order to reach desired properties for the electrodes (e.g. loading, porosity, conductivity) and for the cells (energy and power densities). We have demonstrated this pionneering digital twin for multiple manufacturing steps and electrode chemistries (e.g. NMC111, NMC622, LFP, graphite, silicon-graphite blends), with the strong support of experimental databases acquired in our LIB manufacturing pilot line.[2] We have also demonstrated the transferability of our approach to Sodium Ion and Solid State Batteries, and we start to adapt it for dry processing methods. In my talk, I will present the latest updates of this research and development effort. Furthermore, I will also present the latest updates of the ARTISTIC Online Calculator, a free service to perform battery manufacturing simulations from an internet browser, as well as of our Virtual and Mixed Reality tools to train students, researchers and operators in the battery manufacturing field. These updates will include the presentation of the first "battery manufacturing metaverse", a multi-user battery manufacturing pilot line in Virtual Reality designed to perform practices with the MSc. students of the i-MESC Erasmus+ programme (Interdisciplinarity in Materials for Energy Storage and Coversion).[3]References[1] https://www.erc-artistic.eu/[2] https://www.modeling-electrochemistry.com/publications[3] https://i-mesc.eu/

An Open Access Resource for Marmoset Neuroscientific Apparatus.

The use of the common marmoset (Callithrix jacchus) for neuroscientific inquiry has grown precipitously over the past two decades. Despite windfalls of grant support from funding initiatives in North America, Europe, and Asia to model human brain diseases in the marmoset, marmoset-specific apparatus are of sparse availability from commercial vendors and thus are often developed and reside within individual laboratories. Through our collective research efforts, we have designed and vetted myriad designs for awake or anesthetized magnetic resonance imaging (MRI), positron emission tomography (PET), computed tomography (CT), as well as focused ultrasound (FUS), electrophysiology, optical imaging, surgery, and behavior in marmosets across the age-span. This resource makes these designs openly available, reducing the burden of de novo development across the marmoset field. The computer-aided-design (CAD) files are publicly available through the Marmoset Brain Connectome (MBC) resource (https://www.marmosetbrainconnectome.org/apparatus/) and include dozens of downloadable CAD assemblies, software and online calculators for marmoset neuroscience. In addition, we make available a variety of vetted touchscreen and task-based fMRI code and stimuli. Here, we highlight the online interface and the development and validation of a few yet unpublished resources: Software to automatically extract the head morphology of a marmoset from a CT and produce a 3D printable helmet for awake neuroimaging, and the design and validation of 8-channel and 14-channel receive arrays for imaging deep structures during anatomical and functional MRI.

DRAMS — the new system for predicting type 2 diabetes mellitus remission after baritaric surgery

BACKGROUND: Bariatric surgery has proven effective as a treatment for the metabolic complications of obesity, including type 2 diabetes mellitus (T2DM). Nowadays it is important to develop a personalized approach for bariatric patients generally, and for those with T2DM.AIM: Development of a scale for preoperative prediction of remission of T2DM in obese patients Russian population undergoing bariatric surgery (sleeve gastrectomy or gastric bypass).MATERIALS AND METHODS: A retrospective multicenter cohort study was conducted. The study included 112 patients (75 women and 37 men), mean age 46.25±9.29 years, mean BMI 48.71±7.66 kg/m2. A mathematical analysis of 17 preoperative clinical criteria was carried out to search for independent predictors of T2DM remission.RESULTS: A remission of T2DM was verified in 85 subjects, (75.9%), absence of remission of T2DM — in 27 subjects (24.1%). The most important prognostic factors were T2DM experience, age, HOMA-IR insulin resistance index, HbA1c, BMI, metformin therapy, SUs, insulin intake, increased levels of liver transaminases, and total cholesterol levels. The relative importance coefficient of each parameter was calculated. A formula has been developed to predict the high or low probability of T2DM remission after bariatric treatment. An online DRAMS (Diabetes Remission After Metabolic Surgery) calculator was proposed based on this formula so it is easy to use in clinical practice.CONCLUSION: An online calculator has been developed to predict the probability of T2DM remission after bariatric surgery. The received data allow us to construct a qualitative “high risk”/“low risk” scale. A number of identified predictors require further study in larger samples of patients.

Advantage of Log Odds of Metastatic Lymph Nodes After Curative-Intent Resection of Gallbladder Cancer.

Lymph node metastasis (LNM) is among the most important predictors of poor prognosis after surgery for gallbladder cancer (GBC). Traditionally, staging has been based on the raw count of LNM, with a high risk of understaging patients who undergo inadequate lymph node dissection (LND). The log odds of metastatic lymph nodes (LODDS) may represent an alternative staging approach to stratify patients more accurately after resection of GBC. In this cross-sectional study, patients who underwent curative-intent surgery with LND for GBC were identified from an international database. Two predictive models were built and compared, each integrating a different lymph nodes status indicator [i.e., American Joint Committee on Cancer (AJCC) and LODDS]. Among 199 patients, the median number of lymph nodes examined was 5 [interquartile range (IQR): 3.0, 8.0]; most patients had T1 (n = 26, 13.1%) or T2 (n = 97, 48.7%) disease, and a subset had LNM (n = 87, 44.0%). Multivariable Cox analysis demonstrated LODDS was an independent predictor of overall survival [hazard ratio (HR) 1.84, 95% confidence interval (CI) 1.5-2.3; p < 0.001]. The LODDS model demonstrated better performance compared with a traditional model that utilized the AJCC N category [concordance (C) index: 0.814 versus 0.763; p < 0.001]. Patients classified as high- versus low-risk based on LODDS had much worse overall survival (OS) (4.9% versus 83.7%, respectively; p < 0.001). The LODDS model performance remained high even among patients with inadequateLND (< 6 LN) (C index: 0.87). An online calculator was developed ( https://catalano-giovanni.shinyapps.io/LoddsGBC/ ). A novel prognostic model based on LODDS may overcome the inherent limitations of the current AJCC staging system, reducing understaging among patients with fewer than six total lymph nodes evaluated.

Bone microarchitecture evaluated by HR-pQCT in Chinese adolescent and pediatric patients with X-linked hypophosphatemia.

X-linked hypophosphatemia (XLH) is the most common form of heritable hypophosphatemic rickets. Previous studies have found deteriorated bone microarchitecture in the XLH adults. Detailed studies on the skeletal microarchitecture of XLH adolescent and pediatric patients are still lacking. This study aimed to evaluate bone geometry, density, microarchitecture, stiffness in XLH adolescent and pediatric patients by using high-resolution peripheral quantitative computed tomography (HR-pQCT).Method: This study utilized HR-pQCT to assess bone geometry, density, microarchitecture, and stiffness in 106 Chinese adolescent and pediatric patients with XLH. Compared with the sex- and age-matched controls, XLH patients had significantly higher trabecular area (Tb.Ar), lower total volumetric bone mineral density (Tot.vBMD), lower cortical volumetric BMD (Ct.vBMD), and lower stiffness at both the distal radius and the tibia after adjusting for height and weight. Alkaline phosphatase Z score (ALP-Z), a marker to reflect the disease activity of rickets, was negatively correlated with Ct.vBMD and cortical thickness (Ct.Th) at the distal radius, and Ct.vBMD at the distal tibia, and positively correlated with cortical porosity (Ct.Po) at the distal tibia. We developed an online calculator to estimate Tb.Ar, Ct.vBMD, and stiffness of the distal tibia of XLH adolescent and pediatric patients based on clinical general characteristic and biochemical indicators. The bone microarchitecture of XLH adolescent and pediatric patients was deteriorated, and ALP-Z was negatively correlated with the skeletal quality of XLH adolescent and pediatric patients, especially in the cortical bone. HR-pQCT parameters can be estimated using clinical characteristics and biochemical indicators, which may assist physicians to monitor the disease progression in areas without HR-pQCT access.

BN SO08 - A Comparison of outcomes following Laparoscopic Cholecystectomies before and after COVID-19

Abstract Background The COVID-19 pandemic led to the suspension of elective surgeries between March 2020 and March 2021 in the NHS and large parts of the world. In Prince Charles Hospital, elective services were phased in from April 2021 and elective laparoscopic cholecystectomies resumed in June 2021. There is an almost universal perception among surgeons that the cholecystectomies after covid disruption, have become more technically demanding, resulting in longer operative times and poorer outcomes. This study aims to quantify the differences in indications, CLOC score, Parkland grades, and outcomes, following elective cholecystectomy, one year before and after COVID Method Laparoscopic Cholecystectomies performed on elective lists between January 2019 and December 2019 by two UGI surgeons were included in the pre-COVID group. Cholecystectomies performed between September 2021 and August 2022 by the same UGI surgeons were included in the post-COVID group. Cholecystectomies on the CEPOD list and procedures which included CBD exploration or choledochoscopies were excluded. Demographics, ASA, indications, waiting times, operative times, CLOC and Parkland scores were collected. Outcome measures were complications, drains, hospital stay and proportion of subtotal colectomy, day case surgeries, and conversions to open. An online calculator for social science statistics was used to analyze data. Results The pre-covid(n=92) and post-covid(n=98) groups were comparable in age(P=.37), gender(P=.06) and ASA(P= .057). Pre Covid, lower Proportion of patients underwent cholecystectomy for previous cholecystitis(precovid 13.04%, post-covid 35.7%)(P&amp;lt;.001). Pre Covid Cholecystectomies were shorter(pre-covid 74 minutes, post-covid 85 minutes, P=.018). The mean CLOC score and Parkland grade in precovid group (CLOC= 4.03, Parkland 1.8) were less than in the postcovid group(mean CLOC=5.41, mean Parkland 2.5)( P&amp;lt;.001, P=.006). Apart from the Subtotal Cholecystectomy rate (5.4% pre-covid vs 12.2 postcovid, P=-04) and drains (P=.02), there were no significant differences in outcome measures. Conclusion This study demonstrares that the COVID disruption resulted in more challenging procedures, indicated by the increase in CLOC score, Parkland grade, and longer operating times. This is a reflection of a much larger proportion of surgeries being performed for previous cholecystitis. There are however no significant differences in outcome, except the higher rates of subtotal cholecystectomy. This suggests that the skills and techniques have compensated for higher difficulty levels. Data does show an approximately 15% difference between the groups in all other outcome measures which however does not reach statistical significance. A larger sample is needed to clarify this.

Harmonized Tubular Maximum Phosphate Reabsorption per Glomerular Filtration Rate (TmP/GFR) reporting in adults and children in Australia and New Zealand

Abstract Tubular maximum phosphate reabsorption per glomerular filtration rate (TmP/GFR) is a key diagnostic test for renal phosphate wasting. However, there is a lack of international consensus regarding the reporting of age-related reference intervals. The TmP/GFR Harmonization Working Group, formed by The Australasian Association for Clinical Biochemistry and Laboratory Medicine and The Australian and New Zealand Bone and Mineral Society, aimed to evaluate analytical differences amongst commercial creatinine and phosphate assays, harmonize age-specific cut-offs and compare the ANZBMS TmP/GFR online calculator with existing products. A total of 11 360 results from The Royal College of Pathologists of Australasia Quality Assurance Programs were analyzed to assess creatinine and phosphate assay performance amongst five In Vitro Diagnostic (IVD) companies. Ortho-Clinical Diagnostics analyzers had a positive bias of up to 32% for serum phosphate and up to 29% for TmP/GFR. The other IVD companies produced comparable results and are suitable for harmonized reference intervals. To date, the AACB-ANZBMS TmP/GFR online calculator is the only validated Isotope Dilution Mass Spectrometry-creatinine aligned tool which caters for both pediatric and adult individuals, providing automatic interpretive comments to aid clinicians managing patients with hypophosphatemia.

Inclusive practice: a study exploring community pharmacists' perspectives, knowledge, and training of Neurodiversity (ND) and the effects on their practice

Abstract Introduction An estimated 1 in 7 people in the UK are neurodivergent, a term encompassing conditions such as autism, attention deficit hyperactivity disorder (ADHD), and other learning differences.1 Neurodivergent individuals have poorer health outcomes, increased risk of premature mortality and reduced access to healthcare.2 The Oliver McGowan case highlighted the need for raising awareness of neurodiversity and mandatory training.2 CPs are uniquely positioned to implement simple adjustments, e.g., adapting communication methods and inclusive pharmacy spaces, consequently reducing health inequalities. CPs’ knowledge, cultural perspectives and current accommodations in their practice for neurodiverse patients must be determined. Aim To explore community pharmacists' perspectives, knowledge and training of neurodiversity and the effects on their practice. Methods A mixed-methods approach was used following ethical approval from Kingston University Ethics Committee. After a pilot study, using random sampling, paper questionnaires comprising 26 mixed-style questions were distributed and collected in person to community pharmacists in East London boroughs within a two-month period. Survey participants were invited for an optional in-person or remote semi-structured interview, conducted using a validated interview guide. The minimum sample size was 129 pharmacists (Raosoft online calculator; 95% confidence interval). Data was analysed using descriptive and inferential statistics, using Microsoft Excel alongside thematic analysis for interview responses. Results Response rate was 80% (n=103/129) and 6 pharmacists participated in interviews. 53% (n=55) were Asian, 21% (n=22) Black, and 19% (n=20) White ethnicity. 52% (n=54) identified as male and 47% (n=48) as female. There was a statistically significant correlation between ethnicity and perception of neurodiversity, with conditions taken less seriously in Asian and Black cultures compared to White culture (p&amp;lt;0.001). White participants were statistically more likely to rate their knowledge as good or above in comparison to Asian or Black participants (p=0.01), with 70% (n=14/20) White participants rating their knowledge as good in comparison to 18% (n=4/22) Black and 29% (n=16/55) Asian participants. 100% (n=103/103) and 94% (n=97/103) participants were able to identify autism and ADHD respectively as neurodiverse conditions but only 20% (n=21/103) and 21% (n=22/103) respectively identified dyspraxia and dyscalculia. 94% (n=97/103) stated no changes were made to accommodate neurodiverse patients with only 3% (n=3/103) offering large-print labels or poster formats and 2% (n=2/103) offering quiet areas. Thematic analysis demonstrated the absence of interventions and awareness was commonly due to systemic barriers and lack of specific training. Participants described diverse cultural attitudes and attached stigma to neurodiversity. Discussion/Conclusion There are significant gaps in knowledge about neurodiversity, possibly linked to a lack of formal training opportunities for CPs. Consequently, minimal adjustments are made to accommodate neurodiverse patients. Changes to pharmacy education and training are needed to develop general understanding, with tools that can be incorporated in pharmacies to aid neurodiverse patients and improve health literacy.2 Simple measures such as ‘quiet hours’ or large-text labels can greatly impact a person’s healthcare experience. However, improvements are required in infrastructure and resources within pharmacies, as well as shifts in cultural attitudes to encourage patients to share their status.3 Study limitations included time constraints and a small but diverse geographical area.

Development and validation of an interpretable machine learning model for predicting the risk of distant metastasis in papillary thyroid cancer: a multicenter study

The survival rate of patients with distant metastasis (DM) of papillary thyroid carcinoma (PTC) is significantly reduced. It is of great significance to find an effective method for early prediction of the risk of DM for formulating individualized diagnosis and treatment plans and improving prognosis. Previous studies have significant limitations, and it is still necessary to develop new models for predicting the risk of DM of PTC. We aimed to develop and validate interpretable machine learning (ML) models for early prediction of DM in patients with PTC using a multicenter cohort. We collected data on patients with PTC who were admitted between June 2013 and May 2023. Data from 1430 patients at Yunnan Cancer Hospital (YCH) served as the training and internal validation set, while data from 434 patients at the First Affiliated Hospital of Kunming Medical University (KMU 1st AH) was used as the external test set. Nine ML methods such as random forest (RF) were used to construct the model. Model prediction performance was compared using evaluation indicators such as the area under the receiver operating characteristic curve (AUC). The SHapley Additive exPlanation (SHAP) method was used to rank the feature importance and explain the final model. Among the nine ML models, the RF model performed the best. The RF model accurately predicted the risk of DM in patients with PTC in both the internal validation of the training set [AUC: 0.913, 95% confidence interval (CI) (0.9075-0.9185)] and the external test set [AUC: 0.8996, 95% CI (0.8483-0.9509)]. The calibration curve showed high agreement between the predicted and observed risks. In the sensitivity analysis focusing on DM sites of PTC, the RF model exhibited outstanding performance in predicting "lung-only metastasis" showing high AUC, specificity, sensitivity, F1 score, and a low Brier score. SHAP analysis identified variables that contributed to the model predictions. An online calculator based on the RF model was developed and made available for clinicians at https://predictingdistantmetastasis.shinyapps.io/shiny1/. 11 variables were included in the final RF model: age of the patient with PTC, whether the tumor size is>2cm, whether the tumor size is≤1cm, lymphocyte (LYM) count, monocyte (MONO) count, monocyte/lymphocyte ratio (MLR), thyroglobulin (TG) level, thyroid peroxidase antibody (TPOAb) level, whether the T stage is T1/2, whether the T stage is T3/4, and whether the N stage is N0. On the basis of large-sample and multicenter data, we developed and validated an explainable ML model for predicting the risk of DM in patients with PTC. The model helps clinicians to identify high-risk patients early and provides a basis for individualized patient treatment plans. This work was supported by the National Natural Science Foundation of China (No. 81960426, 82360345 and 82001986), the Outstanding Youth Science Foundation of Yunnan Basic Research Project (No. 202401AY070001-316), Yunnan Province Applied and Basic Research Foundation (No. 202401AT070008), and Ten Thousand Talent Plans for Young Top-notch Talents of Yunnan Province.

Preoperative identification of early extrahepatic recurrence after hepatectomy for colorectal liver metastases: A machine learning approach.

Machine learning (ML) may provide novel insights into data patterns and improve model prediction accuracy. The current study sought to develop and validate an ML model to predict early extra-hepatic recurrence (EEHR) among patients undergoing resection of colorectal liver metastasis (CRLM). Patients with CRLM who underwent curative-intent resection between 2000 and 2020 were identified from an international multi-institutional database. An eXtreme gradient boosting (XGBoost) model was developed to estimate the risk of EEHR, defined as extrahepatic recurrence within 12months after hepatectomy, using clinicopathological factors. The relative importance of factors was determined using Shapley additive explanations (SHAP) values. Among 1410 patients undergoing curative-intent resection, 131 (9.3%) patients experienced EEHR. Median OS among patients with and without EEHR was 35.4months (interquartile range [IQR] 29.9-46.7) versus 120.5months (IQR 97.2-134.0), respectively (p<0.001). The ML predictive model had c-index values of 0.77 (95% CI, 0.72-0.81) and 0.77 (95% CI, 0.73-0.80) in the entire dataset and the validation data set with bootstrapping resamples, respectively. The SHAP algorithm demonstrated that T and N primary tumor categories, as well as tumor burden score were the three most important predictors of EEHR. An easy-to-use risk calculator for EEHR was developed and made available online at: https://junkawashima.shinyapps.io/EEHR/. An easy-to-use online calculator was developed using ML to help clinicians predict the chance of EEHR after curative-intent resection for CRLM. This tool may help clinicians in decision-making related to treatment strategies for patients with CRLM.

Formation of students’ subject skills in mathematics in the process of digital didactic game

The work is devoted to the study of the problem of gamification of mathematics teaching in primary school using methods of studying pedagogical experience, comparing and generalizing the results of observations, analyzing scientific articles and the content and structure of didactic games in mathematics. The urgency of the problem is due to the digital transformation of education currently taking place. Digitalization of education in the context of teaching mathematics in primary school entails the need to develop new learning tools. Such teaching tools should contribute to the development of students’ mathematical skills, while meeting the needs of students of the digital generation for the form of information presentation. Digital didactic games can be used as an effective means of teaching mathematics at school. The potential of such games in the formation of students’ mathematical and logical skills, cognitive motivation is indicated. In the mathematical content of the game, it is proposed to provide for a change of types of educational activities. This will expand the range of skills formed through digital play, make it more dynamic and interesting for students, which will contribute to their involuntary learning. To develop the skills to perform various graphical constructions using the tools of modern graphical calculators, it is proposed to include tasks at certain stages of the game, for which digital tools can be used (online calculators, electronic programs, mobile applications, etc.). The conclusion is made about the expediency of using digital didactic games in the process of teaching mathematics to schoolchildren.