Akaike Information Criterion Research Articles

Impact of surgeon variability on outcomes after total shoulder arthroplasty: an analysis of 2188 surgeons

BackgroundThere has been limited evaluation of surgeon-specific factors on outcomes after total shoulder arthroplasty (TSA). The goals of this study were (1) to determine the impact of surgeon procedural volume, career duration, practice type, and fellowship training on TSA outcomes at 2-year follow-up and (2) to evaluate the relative importance of surgeon variables in relation to patient variables in influencing outcomes. MethodsThe PearlDiver Mariner database was queried to identify all surgeons who performed a minimum of 11 TSA procedures from 2010 to 2018. An Internet search of publicly available data was performed to determine the career duration, practice type, and fellowship training of each surgeon. Multivariate logistic regression models were built to determine the relationship between surgeon-specific variables and 2-year surgical complications and revisions and 90-day readmissions. Variable importance of patient-specific and surgeon-specific factors was determined by the Akaike information criterion increase of these models. ResultsA total of 2188 surgeons who performed 93,122 TSA procedures were identified in this database. Higher reverse TSA surgical volume was associated with fewer surgical complications and revisions, although such a relationship was not observed for anatomic TSA. Revision after anatomic TSA was more common among surgeons who were in their first 5 years of practice. There was a higher risk of surgical complication and readmission among academic surgeons, but this did not equate with a higher risk of revision. The impact of these surgeon-specific factors on outcomes was small in relation to patient-level variables, such as age, sex, and number of medical comorbidities. ConclusionSurgeon procedural volume, career duration, and practice setting influence the surgical complication, revision, and readmission rates after TSA. The impact of surgeon factors was small relative to patient variables, highlighting the importance of patient selection in mitigating adverse outcomes.

Validation of the FIGO2023 staging system for early-stage endometrial cancer

BackgroundIn 2023, the International Federation of Gynecology and Obstetrics (FIGO) updated the endometrial cancer staging system (FIGO2023). Our study aimed to validate the prognostic value of FIGO2023 in patients with early-stage EC (Stage I and Stage II). MethodsAfter screening eligible EC patients from the Surveillance, Epidemiology and End Results (SEER) database, Kaplan-Meier cancer-specific survival (CSS) curves were used to evaluate the prognosis of patients with different stages. In addition, AUC, C-index, Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), and Decision curve analysis (DCA) were used to comprehensively compare the efficacy of the new and the old staging system in predicting prognosis. ResultsA total of 33,156 patients were enrolled. The introduction of FIGO2023 significantly increased the proportion of stage II patients from 5.53 % to 24.76 %. The FIGO2023 defines different substages for patients, which show significant differences in CSS. Compared with FIGO2009, FIGO2023 performed better in discrimination, goodness of fit and clinical decision making. ConclusionCompared with FIGO2009, FIGO2023 had a higher accuracy in predicting CSS in patients with early-stage EC in the SEER database.

Machine learning–assisted matrix-assisted laser desorption/ionization time-of-flight mass spectrometry toward rapid classification of milk products

This study established a method for rapid classification of milk products by combining matrix assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) analysis with machine learning techniques. The analysis of 2 different types of milk products was used as an example. To select key variables as potential markers, integrated machine learning strategies based on 6 feature selection techniques combined with support vector machine (SVM) classifier were implemented to screen the informative features and classify the milk samples. The models were evaluated and compared by accuracy, Akaike information criterion (AIC), and Bayesian information criterion (BIC). The results showed the least absolute shrinkage and selection operator (LASSO) combined with SVM performs best, with prediction accuracy of 100 ± 0%, AIC of -360 ± 22, and BIC of -345 ± 22. Six features were selected by LASSO and identified based on the available protein molecular mass data. These results indicate that MALDI-TOF MS coupled with machine learning technique could be used to search for potential key targets for authentication and quality control of food products.

Modelling and predicting annual rainfall over the Vietnamese Mekong Delta (VMD) using SARIMA

Climate and rainfall are extremely non-linear and complicated phenomena, which require numerical modelling to simulate for accurate prediction. We obtained local historical rainfall data for 12 meteorological stations in the Vietnamese Mekong Delta (VMD) for the 45-year period 1978–2022, to predict annual rainfall trends. A statistical time series predicting technique was used based on the autoregressive integrated moving average (ARIMA) model. We utilized the seasonal ARIMA process of the form (p,1,q)(P,1,Q) for our study area. The best seasonal autoregressive integrated moving average (SARIMA) models were then selected based on the autocorrelation function (ACF) and partial autocorrelation function (PACF), the minimum values of Akaike Information Criterion (AIC) and the Schwarz Bayesian Information (SBC). The seasonal autoregressive integrated moving average model with external regressors (SARIMAX) was discovered, and a series of SARIMA models of various orders were estimated and diagnosed. To evaluate model fitting, we used the Nash–Sutcliffe coefficient (Nash) and the root-mean-square error (RMSE). The study has shown that the SARIMA (1, 1, 1)(2, 1, 1)11 and SARIMA (1, 1, 1)(2, 1, 1)12 model were appropriate for analyzing and forecasting future rainfall patterns at particular meteorological station in the VMD. The results showed the SARIMA model is more reliable and provides more accurate projections than other commonly used statistical methods, notably interval forecasts. We found that interpretable and reliable near-term location-specific rainfall predicts can be provided by the SARIMA-based statistical predicting model.

Allometric modelling to estimate the leaf area of purslane (Portulaca umbraticola Kunth.)

Portulaca umbraticola is a species that has several uses, including ornamental. Among the variables measured in ornamental plants are the biometric data of the leaves, which can be obtained through image analysis, using indirect method, which is simple, accurate and economically viable. This work aimed to propose equations obtained by mathematical models to estimate the leaf area of P. umbraticola. The study was conducted at the Universidade Federal Rural do Semi-Árido, Mossoró, RN, Brazil. Three hundred leaves of each of the five genotypes of P. umbraticola were evaluated for length, width, product between length and width, and leaf area. Determination and correlation coefficients, Akaike information criterion, Willmott agreement index, coefficient of variation, normality using the Shapiro-Wilk test, principal components and Student's t-test were used to systematize the data. Descriptive measurements obtained from the leaf blades of P. umbraticola showed variability between genotypes, demonstrating that the sample was representative for the population analyzed. The regression models obtained were linear and potential. Allometric equations can be used to estimate leaf area in P. umbraticola, and the recommended equations are ŷ = 0.356*L (R² = 0.91) for length, ŷ = 1.665*W (R² = 0.90) for width and ŷ = 0.016 × 0.0711*LW (R² = 0.99), ŷ = 0.715*LW (R² = 0.99) and ŷ = 0.724*LW (R² = 0.99) for the relationship between leaf length and width. The equations to estimate the leaf area of P. umbraticola showed excellent data fits, with values of R² = 0.993 and little dispersion of the data.

Accessing biochemical shifts in a novel Scenedesmus strain via acetaminophen detoxification: Experiment utilizing Box-Behnken optimization and isotherm analysis

Acetaminophen's inherent solubility and hydrophilic nature facilitate its accumulation in aquatic ecosystems. Herein, Scenedesmus dimorphus IITISM-DIX1 demonstrates efficient acetaminophen removal, concurrently serving as a substrate for lipid biosynthesis. Employing Box-Behnken design, optimization of parameters like pH, light duration and concentration of acetaminophen influencing its elimination is executed. Characterization of pre- and post-algal biomass involves FE-SEM, FTIR, and BET analysis. Kinetic and adsorption analyses reveal pseudo-first-order kinetics (R2 = 0.99) and adherence to the Freundlich isotherm (R2 = 0.94). FTIR spectroscopy demonstrates subtle shifts in IR bands post-sorption, indicative of biomass involvement in adsorption processes. Biodegradation and biosorption serve as the main removal pathways, facilitated by exopolysaccharides, generating by-products such as 4-aminophenol, hydroquinone, and formic acid. The Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) values obtained for the Freundlich isotherm validate it as the optimal model, indicating heterogeneous multilayered sorption with efficiency ranging from 44% to 100%. Additionally, exposure to acetaminophen-contaminated media leads to biochemical alterations in Scenedesmus dimorphus IITISM-DIX1. The findings of this study unveil the first elucidated pathway for acetaminophen degradation by any Scenedesmus species, delivering essential knowledge about microalgae-mediated acetaminophen degradation and lipid enrichment mechanisms.

Creating a Lactation Model for 305-Day Milk Yield with Different Resampling Techniques (Bagging Mars) in Mars Modeling

The main purpose of this research is to obtain a prediction model for milk yield by using Multivariate Adaptive Regression Splines (MARS) and Bagging MARS algorithms as a non-parametric regression technique. For this purpose, the effects on milk yield of 305 days were investigated by using lactation parameters in dairy cattle. In the study, 9337 lactation milk yield records belonging to 37 animals belonging to the 2022-2023 period were used and the data set was created by randomly ordering the animals. Data on milk yield results were analyzed with MARS and Bagging MARS algorithms. For dairy cattle; it was modeled with explanatory variables such as lactation month (month), service period (SP), last 7 days average milk yield (L7DMMY), animal's first birth age (FP), animal's age (Age), number of lactations (LN).Correlation coefficient (r), coefficient of determination (R2), Adjusted R2, Root of Square Mean Error (RMSE), standard deviation ratio (SD ratio), mean absolute percent error (MAPE), mean absolute for MARS algorithm estimating total average milk yield deviation (MAD) and Akaike Information Criteria (AIC) values are 0.9986, 0.997, 0.977, 0.142, 0.052, 0.2389, 0.086 and -88, respectively. Similar statistics for the Bagging MARS algorithm are 0.754, 0.556, 0.453, 1.8, 0.666, 3.96, 1.47, and 115, respectively. It has been observed that MARS and Bagging MARS algorithms provide correct results according to the goodness of fit statistics. In this study, it was revealed that MARS algorithm gave better results in milk yield modeling of 305-day lactation.

The M-Estimator and S-Estimator in Robust Improved Geographically and Temporally Weighted Regression for Modelling GRDP in West Java, Indonesia

The success of development in a region in Indonesia can be measured by economic growth, especially in the financial sector using the Gross Regional Domestic Product (GRDP) growth rate. The GRDP figure at the Regency and City level in West Java is one of Indonesia's highest and most diverse. This is due to various factors, including the geographical location of West Java, which is directly adjacent to DKI Jakarta, which is the center of the national economy. Although classified as high, the diversity of GRDP values between regions in West Java needs attention to equalize economic growth. The diversity of GRDP values can be modeled by the Improved Geographically and Temporally Weighted Regression (I-GTWR) method by taking samples in 2018-2022. The I-GTWR modeling method considers the influence of spatial heterogeneity and spatial-temporal interaction, which has been proven to produce better results than the GTWR method in modeling GRDP in Central Java in 2011-2015. This study also adds M-estimators and S-estimators to improve the model's performance and make it robust to outliers. The explanatory variables we use are Regional Original Revenue, General Allocation Fund, Foreign Investment, Regional Minimum Wage, Domestic Investment, Poverty, Per-Capita Expenditure, and Number of Job Vacancies. The analysis shows that the Robust I-GTWR model, especially the M-estimator, produces better model performance than the I-GTWR model in modeling West Java GRDP. The coefficient of determination made by the Robust I-GTWR method using the M-estimator is 94.62% with a mean absolute deviation value of 0.1491 and an Akaike Information Criterion value of 110.4899.

Developing an Innovative Imperfect Debugging Software Reliability Growth Model with Enhanced Testing Coverage Strategies

In this paper, we introduce an innovative Software Reliability Growth Model (SRGM) designed to tackle the pivotal challenges associated with software reliability in the contemporary digital landscape, where the prevalence of online systems is ubiquitous. This SRGM integrates Imperfect Debugging (ID), Testing Coverage (TC), Testing Effort (TE), and error generation into a cohesive framework. Employing a sigmoid function to encapsulate TE, it incorporates three distinct TC functions: Delayed S-shaped, Exponential, and Logistic. This model relies on foundational assumptions, including the proportionality of fault detection rates to remaining faults, the introduction of new faults during debugging, and the intricate connection between fault detection and code coverage. The Mean Value Function (MVF) is computed through these differential equations, and the resultant MVFs are systematically tabulated for all models. An examination of the sigmoid TE function and the Weibull TE function across diverse datasets, utilizing a range of goodness-of-fit criteria including Mean Square Error (MSE), Pham’s Criterion (PC), Predictive Risk Ratio (PRR), Bayesian Information Criterion (BIC), and Akaike’s Information Criterion (AIC), reveals the superior performance of the sigmoid TE function over the Weibull counterpart across various datasets and evaluation criteria. In conclusion, this paper introduces a groundbreaking SRGM that seamlessly integrates ID, TC, and TE, offering valuable insights for assessing software reliability in the dynamic landscape of modern digital systems.

Political skill amplifies the beneficial effects of leader humor on actors’ work engagement via psychological capital

Existing research provides substantial evidence of the positive effects of leader humour on followers and their teams. However, less attention has been given to understanding how such behaviour benefits leaders. This study, grounded in the Conservation of Resources theory, introduces and tests a model that clarifies the mechanisms and conditions under which leader humour benefits these individuals. The study findings, derived from data collected from 137 supervisors in an experience sampling study and 134 supervisors in a three-wave field study, consistently show a positive correlation between leader humour and leader psychological capital, which subsequently enhances leader work engagement. Moreover, the magnitude of this positive effect depends on the leader’s political skill level, with those possessing high political skill reaping greater benefits from leader humour. The results suggest that leader humour is an effective method for supervisors to gain psychological resources.

The role of retinal irradiance estimates in melanopsin-driven retinal responsivity: a reanalysis of the post-illumination pupil response in seasonal affective disorder.

To isolate melanopsin contributions to retinal sensitivity measured by the post-illumination pupil response (PIPR), controlling for individual differences in non-melanopsin contributions including retinal irradiance is required. When methodologies to negate such differences present barriers, statistical controls have included age, baseline diameter, iris pigmentation, and circadian time of testing. Alternatively, the pupil light reflex (PLR) and calculations estimating retinal irradiance both reflect retinal irradiance, while the PLR also reflects downstream pathways. We reanalyzed data from an observational, correlational study comparing the PIPR across seasons in seasonal affective disorder (SAD) and controls. The PIPR was measured in 47 adults in Pittsburgh, Pennsylvania (25 SAD) over 50 seconds after 1 second of red and blue stimuli of 15.3 log photons/cm2/s. The PLR was within 1 second while PIPR was averaged over 10-40 seconds post-stimulus. Two raters ranked iris pigmentation using a published scale. We evaluated model fit using Akaike's Information Criterion (AIC) across different covariate sets. The best-fitting models included either estimated retinal irradiance or PLR, and circadian time of testing. The PLR is collected contemporaneously in PIPR studies and is an individually specific measure of nonspecific effects, while being minimally burdensome. This work extends the prior publication by introducing theoretically grounded covariates that improved analytic model fits based on AIC specific to the present methods and sample. Such quantitative methods could be helpful in studies which must balance participant and researcher burden against tighter methodological controls of individual differences in retinal irradiance.

Development of a novel staging classification for Siewert II adenocarcinoma of the esophagogastric junction after neoadjuvant chemotherapy.

Stage classification for Siewert II adenocarcinoma of the esophagogastric junction (AEG) treated with neoadjuvant chemotherapy (NAC) has not been established. To investigate the optimal stage classification for Siewert II AEG with NAC. A nomogram was established based on Cox regression model that analyzed variables associated with overall survival (OS) and disease-specific survival (DSS). The nomogram performance in terms of discrimination and calibration ability was evaluated using the likelihood-ratio test, Akaike information criterion, Harrell concordance index, time-receiver operating characteristic curve, and decision curve analysis. Data from 725 patients with Siewert type II AEG who underwent neoadjuvant therapy and gastrectomy were obtained from the Surveillance, Epidemiology, and End Results database. Univariate and multivariate analyses revealed that sex, marital status, race, ypT stage, and ypN stage were independent prognostic factors of OS, whereas sex, race, ypT stage, and ypN stage were independent prognostic factors for DSS. These factors were incorporated into the OS and DSS nomograms. Our novel nomogram model performed better in terms of OS and DSS prediction compared to the 8th American Joint Committee of Cancer pathological staging system for esophageal and gastric cancer. Finally, a user-friendly web application was developed for clinical use. The nomogram established specifically for patients with Siewert type II AEG receiving NAC demonstrated good prognostic performance. Validation using external data is warranted before its widespread clinical application.

Prevalence and associated factors of teenage childbearing among Ethiopian women using semi-parametric and parametric proportional hazard and accelerated failure time models

BackgroundTeenage childbearing is a common issue for young people’s sexual and reproductive health in the world, particularly in low-income countries, and affects teenagers between the ages of 13 and 19. According to several academics, adolescent pregnancy accounts for the majority of Ethiopia’s population increase, and there has been little effort to address this threat. This study aimed to determine the prevalence and associated factors of the time to teenage childbearing in Ethiopia.MethodThis paper compares the results of the semi-parametric proportional hazard (PH), parametric PH, and accelerated failure time (AFT) models to find the model that best fits the data. The Akaike Information Criterion (AIC) was used to evaluate the performance of models examined in this investigation. Time to teenage childbearing was the study’s outcome variable, while the analysis considered various independent variables. We analyze data from the 2016 National Demographic Health Survey to assess the influence of different risk factors on teenage pregnancy among Ethiopian women.ResultsOut of the 10,274 teenagers (aged between 13 and 19) who participated in the 2016 survey, 6,430 (62.59%) were parents. The study findings revealed that these teenage parents were influenced by various time-related factors before becoming parents. The log-normal AFT model has the lowest AIC value and hence it is the best fit for this data. Results from this model indicated that significant factors influencing the time to teenage childbearing include the age of the household head, current age of the respondents, region, religion, educational attainment, wealth status, intention to use contraception, and recent sexual activity.ConclusionThis study reveals that 62.59% of surveyed teenagers aged 13 to 19 were parents. Various factors at both the individual and community levels: including the age of the household head, regional differences, religious affiliation, educational level, economic status, contraceptive intentions, and recent sexual activity, determine the time to teenage childbearing. Targeted interventions addressing these factors are essential for reducing teenage pregnancies and supporting adolescent parents effectively.

Performance of clinical and histological prognostic scores for kidney survival in ANCA-associated vasculitis.

Integrating clinical and histological parameters into prognostic scores may enhance the prediction of progression to kidney failure in anti-neutrophil cytoplasm antibodies-associated vasculitis (AAV). This study aimed to evaluate the prognostic performance of histological classifications and scoring systems for kidney survival in AAV. This retrospective cohort study included 101 AAV patients with kidney involvement diagnosed by biopsy and followed for ≥12 months. The main outcome was the time to kidney failure. The prognostic performance of each histological and prognostic score was evaluated using Harrell's C statistic and Akaike's Information Criteria. Among the 101 patients, 37 progressed to kidney failure over a median follow-up of 75 months (IQR 39-123). The Harrell's C statistic was 0.702 (0.620-0.784), 0.606 (0.473-0.738), 0.801 (0.736-0.867), 0.782 (0.706-0.858), and 0.817 (0.749-0.885) for the EUVAS/Berden classification, Mayo Clinic Chronicity Score, Percentage of ANCA Crescentic Score (PACS), ANCA renal risk score (ARRS), and the improved ANCA kidney risk score (AKRiS), respectively. The AKRiS best discriminated the risk of kidney failure progression among subgroups. The AKRiS performance decreased with longer follow-up intervals. Adding the peak estimated glomerular filtration rate attained post-therapy improved the AKRiS performance at all follow-up intervals. Kidney relapses precipitated kidney failure in 71% of cases that progressed after the first year of follow-up. The novel AKRiS enhances the prediction of kidney failure in AAV with kidney involvement. As the prognostic yield of AKRiS decreases over time, a second calculation of AKRiS, including post-therapy kidney function, may improve its long-term performance.

Beyond Personal Factor: A Multilevel Analysis for Predicting the Effect of Moral Self-regulation and School Climate on Muslim Adolescent Aggressivity

Aggressive behaviors among adolescents have been increasingly observed in Indonesian Islamic boarding schools (pesantren), garnering significant attention due to their incongruity with the schools' ethos. Addressing these behaviors in pesantren requires a different approach from mainstream schools due to their unique characteristics. This study investigates the impact of moral self-regulation and school climate on aggressiveness among Muslim adolescents, using a multilevel analysis that considers both individual and institutional factors within the educational setting as units of analysis. This quantitative study involved 428 participants (mean=16.45, SD=1.01) both males and females from ten pesantren in three provinces in Indonesia selected using convenience sampling. Data were collected using the modified version of the aggressivity scale, moral self-regulation scale, and school climate scale. They were analyzed using multilevel regression analysis techniques with the aid of the R lmerTest program. An initial evaluation of the need for multilevel analysis was done by checking the significance of the Likelihood Ratio Test (LRT) results for the Intercept models and Intraclass correlation (ICC) above 5%. LRT and Akaike Information Criterion (AIC) were used as the selection criteria for the best hierarchical models involving random intercepts and random slopes. The study found that better moral self-regulation in students significantly reduces adolescent aggressivity. Additionally, a positive school climate helps prevent aggressive behavior among students. Addressing aggression in Muslim adolescents in Indonesia requires focusing on both individual and school factors that promote positive behavior, emphasizing the importance of moral self-regulation and a supportive school environment.

Measuring Pain-related Behavioral Inhibition and Behavioral Activation System Responses: Further Validity Evidence for the Pain Responses Scale.

The Pain Responses Scale and its Short Form (PRS-SF) were recently developed to assess the affective, behavioral, and cognitive responses to pain based on the behavioral inhibition system (BIS) and behavioral activation system (BAS) model of chronic pain. The purpose of this study was to provide additional tests of the psychometric properties of the PRS-SF in a new sample of individuals with chronic pain. A sample of Spanish adults (N = 190) with chronic non-cancer pain completed a translated version of the PRS-SF and a battery of questionnaires measuring validity criteria hypothesized the be associated with BIS and BAS activation, including measures of sensitivity to punishment, sensitivity to reward, pain intensity, pain interference, catastrophizing, and pain acceptance. Confirmatory factor analysis supported a 4-factor structure for the PRS-SF assessing despondent, escape, approach, and relaxation responses (S-B χ 2 [5] = 1.49, Comparative Fit Index = 0.99, Non-Normed Fit Index = 0.99, root-mean-square error of approximation = 0.051, Akaike Information Criterion = 4113.66), with marginal internal consistency for 1 scale (relaxation) and adequate to good internal consistency for the others. The pattern of associations found between the PRS-SF Scale scores and the validity criterion supports the validity of the instrument. The results provide additional support for the validity of the 4 PRS-SF Scale scores, and the reliability of 3 of the scales. If these findings are replicated in future research, investigators may wish to administer more items from the original Relaxation Scale when assessing this domain to ensure adequate reliability for this scale. The other items from the PRS-SF assessing despondent, escape, and approach responses appear to provide at least adequate reliability. When used in this way, the PRS-SF may be used to measure BIS and BAS responses to pain to: (1) provide further tests of the BIS-BAS model of chronic pain and/or (2) understand the potential mediating effects of BIS and BAS responses on the effects of psychological pain treatments to help determine which specific responses are most responsible for the benefits of treatment, and, therefore, which responses should be specifically targeted to enhance treatment response.

A nomogram to predict the pathological complete response in patients with breast cancer based on the TILs-US score.

The tumor-infiltrating lymphocytes-ultrasonography score is a calculation system for predicting lymphocyte-predominant breast cancers in surgical specimens. A nomogram based on the tumor-infiltrating lymphocytes-ultrasonography score was developed to predict the pathological complete response in breast cancer treated with neoadjuvant chemotherapy. A retrospective evaluation was conducted on 118 patients with breast cancer treated with neoadjuvant chemotherapy at Hiroshima University Hospital. Tumor-infiltrating lymphocytes-ultrasonography scores ≥4 were classified as high. A nomogram was developed using a stepwise logistic regression model for pathological complete response (ypT0 ypN0), based on the smallest Akaike information criterion. The predictive ability and clinical usefulness of the nomogram were also evaluated. Among 118 patients, 34 (28.8%) achieved a pathological complete response, and 52 (44.1%) exhibited high tumor-infiltrating lymphocytes-ultrasonography. In multivariate logistic regression analysis, high tumor-infiltrating lymphocytes-ultrasonography (odds ratio, 6.01; P<0.001), clinical complete response (odds ratio, 4.83; P=0.004) and hormone receptor (odds ratio, 3.48; P=0.038) were independent predictors of pathological complete response. A nomogram based on tumor-infiltrating lymphocytes-ultrasonography score, clinical complete response, hormone receptor and clinical N status was developed. The nomogram showed an area under the curve of 0.831 and a bias-corrected area under the curve of 0.809. The calibration plot showed a good fit between the expected and actual pathological complete response values. Decision curve analysis also showed the clinical utility of the nomogram for predicting pathological complete responses. A nomogram based on the tumor-infiltrating lymphocytes-ultrasonography score exhibited a favorable predictive ability for pathological complete response in patients with breast cancer, which can be useful in predicting the residual disease status after neoadjuvant chemotherapy.

Development and validation of a LASSO prediction model for cisplatin induced nephrotoxicity: a case-control study in China

BackgroundEarly identification of high-risk individuals with cisplatin-induced nephrotoxicity (CIN) is crucial for avoiding CIN and improving prognosis. In this study, we developed and validated a CIN prediction model based on general clinical data, laboratory indications, and genetic features of lung cancer patients before chemotherapy.MethodsWe retrospectively included 696 lung cancer patients using platinum chemotherapy regimens from June 2019 to June 2021 as the traing set to construct a predictive model using Absolute shrinkage and selection operator (LASSO) regression, cross validation, and Akaike’s information criterion (AIC) to select important variables. We prospectively selected 283 independent lung cancer patients from July 2021 to December 2022 as the test set to evaluate the model’s performance.ResultsThe prediction model showed good discrimination and calibration, with AUCs of 0.9217 and 0.8288, sensitivity of 79.89% and 45.07%, specificity of 94.48% and 94.81%, in the training and test sets respectively. Clinical decision curve analysis suggested that the model has value for clinical use when the risk threshold ranges between 0.1 and 0.9. Precision-Recall (PR) curve shown in recall interval from 0.5 to 0.75: precision gradually declines with increasing Recall, up to 0.9.ConclusionsPredictive models based on laboratory and demographic variables can serve as a beneficial complementary tool for identifying high-risk populations with CIN.

Practice list size, workforce composition and performance in English general practice: a latent profile analysis

BackgroundFollowing government calls for General Practices in England to work at scale, some practices have grown in size from traditionally small, General Practitioner (GP)-led organisations to large multidisciplinary enterprises. We assessed the effect of practice list size and workforce composition on practice performance in clinical outcomes and patient experience.MethodsWe linked five practice-level datasets in England to obtain a single dataset of practice workforce, list size, proportion of registered patients ≥ 65 years of age, female-male sex ratio, deprivation, rurality, GP contract type, patient experience of care, and Quality and Outcomes Framework (QOF) and non-QOF clinical processes and outcomes. Latent Profile Analysis (LPA) was used to cluster general practices into groups based on practice list size and workforce composition. Bayesian Information Criterion, Akaike Information Criterion and deliberation within the research team were used to determine the most informative number of groups. One-way ANOVA was used to assess how groups differed on indicator variables and other variables of interest. Linear regression was used to assess the association between practice group and practice performance.ResultsA total of 6024 practices were available for class assignment. We determined that a 3-class grouping provided the most meaningful interpretation; 4494 (74.6%) were classified as ‘Small GP-reliant practices’, 1400 (23.2%) were labelled ‘Medium-size GP-led practices with a multidisciplinary team (MDT) input’ and 131 (2.2%) practices were named ‘Large multidisciplinary practices’. Small GP-reliant practices outperformed larger multidisciplinary practices on all patient-reported indicators except on confidence and trust where medium-size GP-led practices with MDT input appeared to do better. There was no difference in performance between small GP-reliant practices and larger multidisciplinary practices on QOF incentivised indicators except on asthma reviews where medium-size GP-led practices with MDT input performed worse than smaller GP-reliant practices and immunisation coverage where the same group performed better than smaller GP-reliant practices. For non-incentivised indicators, larger multidisciplinary practices had higher cancer detection rates than small GP-reliant practices.ConclusionSmall GP-reliant practices were found to provide better patient reported access, continuity of care, experience and satisfaction with care. Larger multidisciplinary practices appeared to have better cancer detection rates but had no effect on other clinical processes and outcomes. As England moves towards larger multidisciplinary practices efforts should be made to preserve good patient experience.

Measuring complexity for hierarchical models using effective degrees of freedom

AbstractHierarchical models can express ecological dynamics using a combination of fixed and random effects, and measurement of their complexity (effective degrees of freedom, EDF) requires estimating how much random effects are shrunk toward a shared mean. Estimating EDF is helpful to (1) penalize complexity during model selection and (2) to improve understanding of model behavior. I applied the conditional Akaike Information Criterion (cAIC) to estimate EDF from the finite‐difference approximation to the gradient of model predictions with respect to each datum. I confirmed that this has similar behavior to widely used Bayesian criteria, and I illustrated ecological applications using three case studies. The first compared model parsimony with or without time‐varying parameters when predicting density‐dependent survival, where cAIC favors time‐varying demographic parameters more than conventional Akaike Information Criterion. The second estimates EDF in a phylogenetic structural equation model, and identifies a larger EDF when predicting longevity than mortality rates in fishes. The third compares EDF for a species distribution model fitted for 20 bird species and identifies those species requiring more model complexity. These highlight the ecological and statistical insight from comparing EDF among experimental units, models, and data partitions, using an approach that can be broadly adopted for nonlinear ecological models.