Test P-value Research Articles

Enhanced adaptive permutation test with negative binomial distribution in genome-wide omics datasets.

The permutation test has been widely used to provide the p-values of statistical tests when the standard test statistics do not follow parametric null distributions. However, the permutation test may require huge numbers of iterations, especially when the detection of very small p-values is required for multiple testing adjustments in the analysis of datasets with a large number of features. To overcome this computational burden, we suggest a novel enhanced adaptive permutation test that estimates p-values using the negative binomial (NB) distribution. By the method, the number of permutations are differently determined for individual features according to their potential significance. In detail, the permutation procedure stops, when test statistics from the permuted dataset exceed the observed statistics from the original dataset by a predefined number of times. We showed that this procedure reduced the number of permutations especially when there were many insignificant features. For significant features, we enhanced the reduction with Stouffer's method after splitting datasets. From the simulation study, we found that the enhanced adaptive permutation test dramatically reduced the number of permutations while keeping the precision of the permutation p-value within a small range, when compared to the ordinary permutation test. In real data analysis, we applied the enhanced adaptive permutation test to a genome-wide single nucleotide polymorphism (SNP) dataset of 327,872 features. We found the analysis with the enhanced adaptive permutation took a feasible time for genome-wide omics datasets, and successfully identified features of highly significant p-values with reasonable confidence intervals.

Association of race with incidence, characteristics, and mortality from incidental prostate cancer: Analysis of two North American contemporary cohorts.

Non-Hispanic Black (NHB) men are at higher risk both for incidence and mortality from prostate cancer (PCa) compared to Non-Hispanic White (NHW) men, but these findings arise from biopsy-detected PCa reports. We aimed to compare the incidence, subsequent management and cancer-specific mortality (CSM) of incidental PCa among NHB and NHW men, using two different North American cohorts. The Surveillance, Epidemiology and End-Result (SEER: 2004-2017) and our institutional Henry Ford Health (HFH: 1995-2022) databases were queried to identify men diagnosed with incidental PCa. Cumulative incidence estimates were used to calculate CSM differences between NHB and NHW men. Competing-risk multivariable regression analysis tested the impact of race on CSM, after accounting for all available covariates. A total of 418 and 6,124 incidental PCa cases were recorded in HFH and SEER database respectively. No pathological differences were observed between NHB and NHW men in both the cohorts, except for prostate-specific antigen (PSA) value at diagnosis, which was higher in NHB men. At 10-years, the CSM rates were 5.5% vs 7.2% in our cohort and 8.6% vs 10.3% in the SEER cohort for NHW and NHB men, respectively (all Gray's test p-value > 0.05). At multivariable, race was not an independent predictor of CSM in our HFH cohort (HR: 1.46, 95% CI: 0.57-3.71, p = 0.6). In the SEER cohort, NHB men were 34% less likely to die from PCa from 1 year to the next (95% CI: 0.49-0.90, p = 0.008), when compared with NHW men. In the comparison of incidental PCa findings between NHB and NHW men, both groups had similar pathological characteristic and survival outcomes. These findings are different from the 'conventional' screening-detected PCa and suggest that racial differences have minimal to no adverse effects on PCa-specific mortality after incidental diagnosis.

One-year prognosis difference of MINOCA versus MI-CAD in developing countries: insights from the Moroccan experience

Abstract Introduction The debate remains open as to the difference in prevalence of mortality and occurrence of acute events in patients with Myocardial infarction with non-obstructive coronary arteries (MINOCA) and others with Myocardial infarction with coronary arteries disease (MI-CAD) (1). Purpose We carried out an analysis of the clinical characteristics and one-year prognosis of MINOCA versus MI-CAD. Methods We conducted a prospective study for patients admitted for Acute coronary syndrome (ACS) during 2-years, to analyze the clinical and prognostic characteristics of patients with MINOCA versus MI-CAD. We defined 1-year all-cause mortality as the primary outcome, and the secondary outcome as a composite of 1-year readmission for myocardial infarction or acute heart failure (AHF). The primary and secondary outcomes was studied using a Cox proportional hazards regression model to estimate hazard ratios (HRs) and 95% confidence intervals (CIs), adjusting for variables described in the literature. Results Our study included 1077 patients, 95.3% with MI-CAD and 4.7% with MINOCA. At admission, 71.1% patient were diagnosed STEMI and 28.9% with NSTEMI. The difference between the two groups was found on age (p<0.001), hypertension, diabetes with consecutive p-values of 0.007 and 0.001, as well as Ejection fraction (p<0.001). For the outcomes studied, we found, firstly before adjustment, that patients in the MINOCA group had a better prognosis than the MI-CAD group, for all-cause 1-year mortality (HR=0. 442; 95%CI 0.227-0.760; p=0.041) with a significant difference in the Kaplein Meier survival analysis with a log-rank test (p-value =0.02) (Figure 1), and a lower risk of readmission for ACS (HR=0.297; 95%CI 0.316-0.821; p=0.034), as well as for readmission for AHF (HR=0.555; 95%CI 0.329-0.836; p=0.045) also with a significant difference between the two groups in the Kaplein Meier analysis with consecutive log-rank test p-values of 0.033 and 0.035 (Figure 2). After adjustment for age, arterial hypertension, diabetes, active smoking, EF<40%, proximal LAD or LM as culprit artery, TIMI 0-I flow, CRP >300mg and cardiogenic shock during hospitalization, the results remained significantly in favor of a better prognosis for MINOCA: (HR=0. 672; 95%CI 0.376-0.872; p=0.003) for 1-year all-cause mortality, (HR=0.301; 95%CI 0.482-0.879; p=0.012) for readmission for ACS and finally (HR=0.604; 95%CI 0.309-0.812; p=0.039) for readmission for AHF. Conclusion Despite the ambiguity in the genesis of MINOCA, the short- and long-term prognosis of these patients remains generally favorable.Figure 1.Figure 2.

Mental health and resilience of students with a refugee background in Greece.RaRE project findings

Abstract Background Research highlights the mental health challenges faced by children and adolescents from refugee backgrounds due to various risk factors encountered during migration, including exposure to violence, family disruptions, insecure legal status, and educational interruptions. These factors increase the risk of psychological issues and academic difficulties. This study investigates the relationship between mental health and resilience in refugee adolescents in Greece. Methods The study focuses on refugee adolescents aged 12-18 from Syria, Iraq, Afghanistan, Somalia, and Ukraine, the countries with the highest numbers of school-age refugees in Greece. Data were collected from 37 refugee children, 37 teachers, 4 parents, and 19 guardians using Poisson sampling. Results are based on responses to the Strengths and Difficulties Questionnaire (SDQ) and the Child Youth Resilience Measure (CYRM), with triangulation of self-completed questionnaires from students, parents/legal guardians, and teachers. Pearson correlation coefficient was employed for analysis. Results We present the pairwise Pearson correlation coefficients of students’ responses to psychometric tests in relation to those of teachers, along with the corresponding p-values for a two-tailed test of significance. Specifically, cor(SDQst,SDQt)=0.443 (p-value=0.006), cor(SDQst,CYRMt)=-0.341 (p-value=0.039), cor(CYRMst,SDQt)=-0.333, (p-value=0.006), cor(CYRMst,CYRMt)=0.323 (p-value=0.051). All p-values are below 0.05 or very close to it, so we reject (or we are close to) the null hypothesis of no association at 5% significance level for every case. On the contrary, we note that the corresponding coefficients between students and parents/legal guardians are all statistically non-different from 0 at 5% significance level. Conclusions Educators exhibit a solid understanding of students’ challenges and resilience compared to parents and guardians. These promising findings support further research.

The triglyceride-glucose index is a marker of thromboembolic events in atrial fibrillation patient under oral anticoagulation therapy

Abstract Background Atrial fibrillation (AF) increases the risk of stroke and thromboembolism; and appropriate risk stratification is central for better targeted patient management. The triglyceride-glucose (TyG) index indirectly assesses insulin resistance, and has been associated with cardiovascular risk. Purpose To investigate whether the TyG index could be a biomarker for assessing the risk of thromboembolic events in patients with AF. Methods We performed an observational prospective cohort study including outpatients diagnosed with AF starting vitamin K antagonist (VKA) or direct-acting oral anticoagulants (DOAC) therapy from January, 2016 to November, 2021. The primary endpoint was a combination of thromboembolic events, considered as the composite of myocardial infarction, and/or venous thromboembolism, and/or ischemic stroke/transient ischemic attack, over 2 years of follow-up. The TyG index was calculated as the natural logarithm (Ln) of the product of baseline plasma glucose and triglyceride using the following formula: Ln [fasting triglycerides (mg/dL) × fasting glucose (mg/dL)]/2. Patients were stratified into tertiles according to their TyG index (ie. first tertile: TyG index <4.59; second tertile: TyG index 4.59-4.83; third tertile: TyG index >4.83). Results We included 2907 patients (median age 77 [70-83] years; 52.5% female). The median TyG index was 4.71 (4.53-4.91), and 34% (989) where in the third tertile of TyG index. During a follow-up of 2 years, 209 (7.2%) patients suffered a thromboembolic event. Compared to patients in the first or second tertiles of TyG index, patients in the third tertile had an increased annual event rate of thromboembolic events (3.40 per 100 patient-years vs. 4.76 per 100 patient-years, p=0.043; and 3.41 per 100 patient-years vs. 4.76 per 100 patient-years, p=0.044), and higher incidence rate ratio (1.41 [95% CI 1.01-1.98], p=0.044 vs. first tertile; 1.40 [95% CI 1.01-1.97], p=0.044 vs. second tertile). Cox regression analyses adjusted for several risk factors demonstrated that the TyG index was an independent predictor for thromboembolic events (aHR 1.82; 95% CI 1.15-2.89; p=0.011). Patients in the third tertile of TyG index presented a 64% higher risk of thromboembolic events (aHR 1.64; 95% CI 1.17-2.29; p=0.004) (Table), with significantly lower event free survival (log-rank test p-value = 0.016) (Figure). Conclusions In this real-world cohort of AF patients under oral anticoagulation therapy, elevated TyG index was an independent risk factor for thromboembolic events. The TyG index is a simple, low-cost indicator, that could be used as a screening tool in everyday clinical practice.

Prognostic value of soluble urokinase plasminogen activator receptor (suPAR) in patients admitted with acute heart failure

Abstract Introduction The inflammatory marker, soluble urokinase plasminogen activator receptor (suPAR), has emerged as a promising prognostic biomarker of heart failure (HF). Higher levels of suPAR in plasma are indicative of hospital readmissions and mortality. Currently little is known about suPAR-values upon admission in patients with acute heart failure (AHF). Purpose This study investigates the prevalence of elevated suPAR and prognostic value in patients hospitalized with AHF. Methods In this prospective cohort study, all patients ≥ 18 years old admitted at the Emergency Department at a large University Hospital in Denmark, were consecutively included from March 10, 2020, to March 31, 2022. The follow-up period was 365 days. The biomarker suPAR was measured in all patients upon admission (median time from admission to suPAR measurement was two hours (IQR 3: 3,9-6,9)). Patient data was extracted from the electronic patient record system, and a cardiologist adjudicated whether the patients had AHF. Patients were stratified according to validated cut-off values of suPAR above or below 6 ng/mL. Kaplan-Meier survival analysis and Cox Regression were used to assess the association between suPAR levels and one-year mortality outcomes. Results A total of 408 (5%) AHF patients, from a total population of 6,715, were included in the study. 40 patients were excluded due to missing suPAR-values and the final analysis comprised of 368 AHF patients. Among AHF patients upon admission, the mean suPAR value was 6.2 ng/mL (standard deviation [SD] = 4.8 ng/mL), and non-AHF patients had a mean value of 4.5 ng/mL (SD = 3.1 ng/mL). A suPAR level above 6 ng/mL was significantly associated with mortality (figure 1), log-rank test p-value < 0.0001. Furthermore, Cox regression analysis demonstrated that elevated suPAR levels were independently associated with increased risk of mortality with a hazard ratio (HR): 2.09 (95% CI: 1.59-2.90), p-value < 0.001 after adjusting for potential confounders: age, sex, atrial fibrillation, Chronic Obstructive Pulmonary Disease, and Creatinine levels. Conclusion The inflammatory biomarker suPAR measured upon hospital admission in AHF patients is significantly associated with one-year mortality in patients admitted with AHF. The potential of suPAR as a valuable prognostic marker in AHF is offering clinicians a tool to identify high-risk patients and tailor management strategies, accordingly, thereby improving patient outcomes.Figure 1 - 1 year mortality

Does PhenoAge acceleration impact adverse outcomes following major surgery (rehospitalization or death)? A genome-wide association study and mendelian randomisation

Abstract Background Biological aging reflects the state of an individual's cellular and molecular function, and is distinct from chronological age; which reflects the passage of time[1]. Validated measures of biological aging, such as Phenotypic aging (PhenoAge) estimated from nine routine clinical biomarkers, have been associated with increased risk of age-related diseases and mortality in cohort studies[2]. However, the potential causal relationship between them remains unclear. In this study, we aim to identify genetic variants associated with PhenoAge Acceleration (PhenoAgeAccl) and adverse outcome after surgery (rehospitalisation or death within 365-days] and assess the causal relationship between these two phenotypes using Mendelian Randomization framework. Objectives -Perform a genome-wide association study(GWAS) on adverse outcomes following major surgery. -Assess the potential causal relationship between PhenoAgeAccl and adverse outcomes following major surgery using Mendelian randomization analysis. Methods We conducted a GWAS via plink using participants from the UKBiobank (Application 77596) to generate summary statistics for 6509 participants who had undergone major surgery within 365 days of their baseline assessment date(3). For quality control, SNPs were excluded if they didn't meet the following criteria: (1) imputation information score <0.3 (2) minor allele frequency <1%, (3) Hardy–Weinberg equilibrium test p-value significant at the Bonferroni-corrected level. Summary statistics for PhenoAgeAccl was obtained from a previous GWAS of European descents[4]. Gene enrichment analysis was performed using Multi-marker Analysis of GenoMic Annotation (MAGMA) in Functional Mapping and Annotation (FUMA). Then, genetic instruments for PhenoAgeAccl and adverse outcomes following surgery were identified to conduct a summary-based Mendelian Randomisation [Assumptions are listed in Figure 1]. Results GWAS for adverse outcomes following major surgery identified 3 lead SNPs (rs1360618, rs11848297, rs7978) [Figure 2]. Gene-set analysis showed enrichment in the immune system, cell migration, fear response, and iron metabolism. For PhenoAge Acceleration, gene-set analysis showed enrichment in immune system, and cell signalling pathways. 17 SNPs were selected which were used for the Two-Sample MR. We found no causal relationship, as the beta coefficient for Inverse variance weighted was -0.308 [standard error - 0.075, p=0.68]. For every 5-year increase in PhenoAge compared to chronological age, the risk of emergency re-hospitalisation or death increased by 21% (HR 1.16 – 1.3, p < 0.001). Conclusion Gene-set analysis for adverse outcomes following major surgery showed enrichment in immune system, iron metabolism, and fear response which have all recently been associated with mortality post-surgery in cohort studies(4,5). No causal relationship between PhenoAgeAccel and mortality post-surgery was established.Mendelian Randomisation assumptionsManhattan plot adverse-outcomes post-sur

Influence of Nutrition Education with Booklet Media on the Knowledge of Nutrition in Athletes Pencak Silat in Pwnu Surabaya

Pencak Silat athletes face challenges in nutritional knowledge which affects their performance. This study aims to assess the impact of nutrition education via booklets on their knowledge. This study employed experimental research using a one group pre- and post-testing. Data collection was conducted using a questionnaire and statistical tests using the Wilcoxon Test. The respondents used amounted to 36. The result showed that the Wilcoxon Test p-value (Asymp.Sig 2-Tailed) was 0.000 <0.05, Therefore, there was an impact on the knowledge of martial arts athletes at PWNU Surabaya on nutrition education through booklet media. The conclution is nutrition education using booklet media significantly affects the increase in knowledge of martial arts athletes at PWNU Surabaya.

How far do oral lectures at European Orthodontic Society congresses reach? A comparison of abstract publication rates, article citations, and social media mentions.

This study investigated the scientific impact of oral abstracts presented in five consecutive European Orthodontic Society (EOS) congresses in terms of full-text publication rates as well as citations and social media metrics (altmetrics) of the resulting articles. PubMed (http://www.ncbi.nlm.nih.gov/pubmed) and Google Scholar (http://scholar.google.nl/) were screened to identify published articles originally presented as lectures at EOS 2015-2019. EOS date, abstract title, subject, number of authors, authors' names, first author's country of origin, and type of affiliation were extracted from congress abstract books. Altmetric Attention Scores (AASs), number of X (formerly Twitter) posts, Mendeley reads, and citations of the articles were retrieved from Altmetric Explorer and Web of Science (WoS) databases, respectively. Abstract details, full publication rates and Altmetric mentions and WoS citations of the related articles were analysed using univariable and multivariable logistic regression. In total, 51.79 to 73.91% of the lectures presented at EOS 2015-2019 were converted into peer-reviewed articles. There was a median interval of 10.95 months (interquartile range 20.5 months) between conference and publication dates. EOS congress year (overall Wald test P-value = .04) and number of authors (overall Wald test P-value < .01) were significant predictors for full-text publication of oral lectures. There was no significant effect of EOS presentation on AASs, citations, X posts, and Mendeley readers (P > .05). Journal impact factors and quartile rankings were not considered to determine the impact of the journals that published articles originally presented as EOS oral abstracts. Overall, 61.30% of EOS 2015-2019 lectures were published as full-length articles. Multi-authored abstracts presented higher odds of publication, whereas oral abstracts presented at EOS 2015 and 2018 had the lowest probabilities to reach full publication. EOS abstract-based articles were assigned similar numbers of citations and AASs to articles not presented at EOS congresses.

Risk factors for identifying pneumocystis pneumonia in pediatric patients.

This study aimed to identify the risk factors and construct the diagnostic model associated with pneumocystis pneumonia (PCP) in pediatric patients. This retrospective observational study analyzed 34 cases of PCP and 51 cases of other types of pneumonia treated at Children's Hospital Affiliated to Shandong University between January 2021 and August 2023. Multivariate binary logistic regression was used to identify the risk factors associated with PCP. Receiver operating characteristic curves and calibration plots were constructed to evaluate the diagnostic model. Twenty clinical variables significantly differed between the PCP and non-PCP groups. Multivariate binary logistic regression analysis revealed that dyspnea, body temperature>36.5°C, and age<1.46 years old were risk factors for PCP. The area under the curve of the diagnostic model was 0.958, the P-value of Hosmer-Lemeshow calibration test was 0.346, the R2 of the calibration plot for the actual and predicted probability of PCP was 0.9555 (P<0.001), and the mean Brier score was 0.069. In addition, metagenomic next-generation sequencing revealed 79.41% (27/34) and 52.93% (28/53) mixed infections in the PCP and non-PCP groups, respectively. There was significantly more co-infection with cytomegalovirus and Streptococcus pneumoniae in the PCP group than that in the non-PCP group (p<0.05). Dyspnea, body temperature>36.5°C, and age<1.46 years old were found to be independent risk factors for PCP in pediatric patients. The probability of co-infection with cytomegalovirus and S. pneumoniae in the PCP group was significantly higher than that in the non-PCP group.

Neural networks pipeline for quality management in IVF laboratory

This study introduces a novel neural network-based pipeline for predicting clinical pregnancy rates in IVF treatments, integrating both clinical and laboratory data. We developed a metamodel combining deep neural networks and Kolmogorov-Arnold networks, leveraging their complementary strengths to enhance predictive accuracy and interpretability. The metamodel achieved robust performance metrics after training and fitting on 11500 clinical cases: accuracy = 0.72, AUC = 0.75, F1 score = 0.60, and Matthews Correlation Coefficient of 0.42. According to morpho-kinetical embryo evaluation, our model’s PRC of 0.66 significantly improves over existing time-lapse systems for pregnancy prediction, demonstrating better handling of imbalanced clinical data. The metamodel’s calibration metrics (Brier score = 0.20, expected calibration error = 0.06, maximum calibration error = 0.12, Hosmer-Lemeshow test p-value = 0.06) indicate robust reliability in predicting clinical pregnancy outcomes. We validated the model’s reproducibility using an independent dataset of 665 treatment cycles, showing close alignment between predicted and actual pregnancy rates (58.9% vs. 59.1%). With the Bayesian method, we proposed a robust framework for integrating historical data with real-time predictions from neural networks, enabling a transition from retrospective to prospective analysis. Our approach extends beyond conventional embryo selection, incorporating post-analytical phase evaluation in the IVF laboratory. This comprehensive framework enables detailed analysis across different patient subpopulations and time periods, facilitating the identification of systemic issues and IVF protocol optimization. The model’s ability to track pregnancy probabilities over time and staff members allows for both outcome prediction and retrospective and prospective assessment of IVF treatment efficacy, providing a data-driven strategy for continuous improvement in assisted reproductive technology.

Performance of Closed-Ended Mutual Funds: A Test of Market Efficiency

Background: Mutual funds, known for aggregating funds from a diverse investor base, play a crucial role in managing small funds smartly. This study evaluates the performance of selected closed-ended mutual funds and examines market efficiency in Nepal. Methods: Using a descriptive and casual-comparative research design, the study spans forty-eight months from January 15, 2018, to January 14, 2022. Monthly data from five mutual funds are analyzed, with metrics such as the Treynor ratio, Sharpe ratio, and Jensen alpha. The study variables include market returns, assets, expense ratios, fund age, liquidity, and mutual fund returns. Various statistical tests, including correlation analysis, Integrated Ranking Analysis, ANOVA test, t-test, and P-value tests, are conducted to determine the significance and statistical relevance of the study variables. Results: The findings reveal that mutual fund performance is influenced by factors such as return, age, liquidity, asset, and expense ratio. Expense ratio and age emerge as the most impactful factors, demonstrating a statistically significant relationship with market return (p &lt; 0.05). All selected mutual funds outperform the market return (NEPSE), with Sanima Equity Fund securing the top rank in all three measures. Conclusion: The Sharpe ratio, Treynor ratio, and Jensen alpha measures for mutual funds surpass those of the market, indicating market inefficiency in its semi-strong form. Novelty: This study uniquely evaluates closed-ended mutual funds using comprehensive performance metrics, demonstrating their significant outperformance compared to market benchmarks. It underscores their effectiveness in actively managing passive funds and challenges the efficient market hypothesis.

Detecting bubbles via FDR and FNR based on calibrated p-values

Detecting bubbles in asset prices is still an open question that has attracted considerable attention in recent years. This paper improves the bubble detection and dating approaches developed in recent years by Phillips and co-authors, proposing to assess the plausibility of its outcomes via the false discovery rate (FDR) and the false non-discovery rate (FNR) based on calibrated p-values. Calibrating p-values of unit root tests, applied sequentially to detect bubbles, allows recovery of their super-uniformity property, which is crucial for a valid implementation of the inferential procedure. The paper also develops original self-calibrated versions of both FDR and FNR for the specific problem of bubble testing. Calibrated p-values are implemented in an online false discovery-based approach which monitors bubbles in real time. The effectiveness of the proposed methods is investigated via a simulation study and an empirical application.

ELiAH: the atlas of E3 ligases in human tissues for targeted protein degradation with reduced off-target effect.

The development of therapeutic agents has mainly focused on designing small molecules to modulate target proteins or genes which are conventionally druggable. Therefore, targeted protein degradation (TPD) for undruggable cases has emerged as promising pharmaceutical approach. TPD, often referred PROTACs (PROteolysis TArgeting Chimeras), uses a linker to degrade target proteins by hijacking the ubiquitination system. Therefore, unravel the relationship including reversal and co-expression between E3 ligands and other possible target genes in various human tissues is essential to mitigate off-target effects of TPD. Here, we developed the atlas of E3 ligases in human tissues (ELiAH), to prioritize E3 ligase-target gene pairs for TPD. Leveraging over 2900 of RNA-seq profiles consisting of 11 human tissues from the GTEx (genotype-tissue expression) consortium, users of ELiAH can identify tissue-specific genes and E3 ligases (FDR P-value of Mann-Whitney test < .05). ELiAH unravels 933 830 relationships consisting of 614 E3 ligases and 20 924 of expressed genes considering degree of tissue specificity, which are indispensable for ubiquitination based TPD development. In addition, docking properties of those relationships are also modeled using RosettaDock. Therefore, ELiAH presents comprehensive repertoire of E3 ligases for ubiquitination-based TPD drug development avoiding off-target effects. Database URL: https://eliahdb.org.

Effect of Virtual Casts Superimposition Strategies on the Estimation of IOS Accuracy in Complete-Arch Scans.

This study aims to assess the impact of three different strategies of best-fit (BF) alignments of virtual casts on the estimation of the accuracy of intraoral scanner (IOS) in complete-arch scans. A maxillary typodont, modified with an Implant Scan Body (ISB) in the retroincisive area, was digitized using a desktop scanner (SW Optor Lab) to obtain a reference STL file. The typodont was then scanned 10 times using two IOSs (Trios4, Itero). Each STL file obtained from the IOS was superimposed onto the reference cast using three methods in CloudCompare: full-arch BF (BF-full), BF at the starting tooth (BF-tooth), and BF at the ISB (BF-ISB). Discrepancies from the reference were recorded, and trueness and precision were compared for each method. Statistical analysis with the Kruskall-Wallis nonparametric test was performed (α = 0.05). The Kruskall-Wallis test (p ⟨0.05) revealed statistically significant differences in trueness and precision among the alignment methods. Post Hoc multiple comparison test p-values were all below the critical alpha value. Differences in BF methods lead to significantly different accuracy values of IOS complete-arch scans as different virtual casts' alignment discrepancy. BF-full had the highest accuracy followed by BF-ISB and BF-tooth.

Risk factor analysis and prediction model construction for severe adenovirus pneumonia in children

BackgroundSevere adenovirus pneumonia in children has a high mortality rate, but research on risk prediction models is lacking. Such models are essential as they allow individualized predictions and assess whether children will likely progress to severe disease.MethodsA retrospective analysis was performed on children with adenovirus pneumonia who were hospitalized at the Children’s Hospital of Nanjing Medical University from January 2017 to March 2024. The patients were grouped according to clinical factors, and the groups were compared using Ridge regression and multiple logistic regression to identify risk factors associated with severe adenovirus pneumonia. A prediction model was constructed, and its value in clinical application was evaluated.Results699 patients were included in the study, with 284 in the severe group and 415 in the general group. Through the screening of 44 variables, the final risk factors for severe adenovirus pneumonia in children as the levels of neutrophils (OR = 1.086, 95% CI: 1.054‒1.119, P < 0.001), D-dimer (OR = 1.005, 95% CI: 1.003‒1.007, P < 0.001), fibrinogen degradation products (OR = 1.341, 95% CI: 1.034‒1.738, P = 0.027), B cells (OR = 1.076, 95%CI: 1.046‒1.107, P < 0.001), and lactate dehydrogenase (OR = 1.008, 95% CI: 1.005‒1.011, P < 0.001). The value of the area under the receiver operating characteristic curve was 0.974, the 95% CI was 0.963–0.985, and the P-value of the Hosmer-Lemeshow test was 0.547 (P > 0.05), indicating that the model had strong predictive power.ConclusionIn this study, the clinical variables of children with adenovirus pneumonia were retrospectively analyzed to identify risk factors for severe disease. A prediction model for severe disease was constructed and evaluated, showing good application value.

External validation and calibration of risk equations for prediction of diabetic kidney diseases among patients with type 2 diabetes in Taiwan

BackgroundMost existing risk equations for predicting/stratifying individual diabetic kidney disease (DKD) risks were developed using relatively dated data from selective and homogeneous trial populations comprising predominately Caucasian type 2 diabetes (T2D) patients. We seek to adapt risk equations for prediction of DKD progression (microalbuminuria, macroalbuminuria, and renal failure) using empiric data from a real-world population with T2D in Taiwan.MethodsRisk equations from three well-known simulation models: UKPDS-OM2, RECODe, and CHIME models, were adapted. Discrimination and calibration were determined using the area under the receiver operating characteristic curve (AUROC), a calibration plot (slope and intercept), and the Greenwood-Nam-D’Agostino (GND) test. Recalibration was performed for unsatisfactory calibration (p-value of GND test < 0.05) by adjusting the baseline hazards of risk equations to address risk variations among patients.ResultsThe RECODe equations for microalbuminuria and macroalbuminuria showed moderate discrimination (AUROC: 0.62 and 0.76) but underestimated the event risks (calibration slope > 1). The CHIME equation had the best discrimination for renal failure (AUROCs from CHIME, UKPDS-OM2 and RECODe: 0.77, 0.60 and 0.64, respectively). All three equations overestimated renal failure risk (calibration slope < 1). After rigorous updating, the calibration slope/intercept of the recalibrated RECODe for predicting microalbuminuria (0.87/0.0459) and macroalbuminuria (1.10/0.0004) risks as well as the recalibrated CHIME equation for predicting renal failure risk (0.95/-0.0014) were improved.ConclusionsRisk equations for prediction of DKD progression in real-world Taiwanese T2D patients were established, which can be incorporated into a multi-state simulation model to project and differentiate individual DKD risks for supporting timely interventions and health economic research.

A newly developed, easy-to-use prehospital drug-derived score compared with three conventional scores: A prospective multicenter study.

The use of medications by emergency medical services (EMS) is increasing. Conventional scores are time-consuming and therefore difficult to use in an emergency setting. For early decision-making, an easy-to-use score based on the medications administered by the EMS may have prognostic value. The primary objective of this study was to develop the prehospital drug-derived score (PDDS) for 2-day mortality. A prospective, multicenter, ambulance-based cohort study was conducted in adults with undifferentiated acute diseases treated by EMS and transferred to the emergency department. Demographic data, prehospital diagnosis data, prehospital medication and variables for the calculation of the National Early Warning Score 2 (NEWS2), Rapid Emergency Medicine Score (REMS), and Rapid Acute Physiology Score (RAPS) were collected. The PDDS was developed and validated, establishing three levels of risk of 2-day mortality. The predictive capability of each score was determined by the area under the curve of the receiver operating characteristic curve (AUROC) and compared using the Delong's test (p-value). A total of 6401 patients were included. The PDDS included age and the use of norepinephrine, analgesics, neuromuscular blocking agents, diuretics, antihypertensive agents, tranexamic acid, and bicarbonate. The AUROC of PDDS was .86 (95% CI: .816-.903) versus NEWS2 .866 (95% CI: .822-.911), p = .828; versus REMS .885 (95% CI: .845-.924), p = .311; versus RAPS .886 (95% CI: .846-.926), p = .335, respectively. The newly developed easy-to-use prehospital drug-derived PDDS score has an excellent predictive value of early mortality. The PDDS score was comparable to the conventional risk scores and therefore might serve as an alternative score in the prehospital emergency setting.

A new frontier in biostatistics: evaluating the accuracy of ChatGPT-4 vs. R in analysing liver resection data

Background The rise of ChatGPT-4’s Data Analyst tool presents a new frontier for biostatistical computations. This study evaluates the reliability and improvements of ChatGPT-4 Data Analyst tool by comparing it to R package in performing biostatistical analysis on liver surgery patients. Methods Utilizing data from LiverGroup.org, we conducted our comparative study between October 2023 and March 2024. The variables analyzed by the R package and ChatGPT-4 Data Analyst included age, sex, hospital stay duration, income group, and mortality. Analysis on ChatGPT-4 were performed using two methods: a holistic prompt which included all-at-once analysis were requested and segmented prompts, one-by-one test request for analysis. After the analysis figures were requested from ChatGPT-4, comparison with R package figures was done. Results Descriptive analysis including N (%), Standard Deviation, and (25th–75th Percentile) were consistent between ChatGPT-4 March version and R with a minor variation in the holistic approach on the analysis performed in October. The inferential statistical results of ChatGPT-4 showed inconsistencies in October 2023 while March 2024 revealed accurate results with Crosstabulations, Kruskal Wallis, Wilcoxon Rank Sum, T-test, Pearson’s Chi-squared, and Fisher’s Exact test p-value. ChatGPT-4 March 2024 version was able to inform the user with possible inaccuracies in certain tests (Mann-Whitney U Test: Hospital stay vs mortality p value, Levene’s Test p-value: Age vs mortality, and Fisher’s Exact Test: Odds ratio gender vs. mortality 95% CI). The survival curve and box-and-whisker plot generated by ChatGPT-4 in March 2024 matched those generated by R package except for the CI of survival curve. Conclusions The high accuracy of ChatGPT-4 in certain biostatistical analysis has reached the point where it can replace established statistical software like R for some purposes. Artificial intelligence tools show significant promise but should still be used in conjunction with traditional methods to ensure precision in complex analysis. Consensus on the use of these tools is needed by the scientific community.

Development and validation of a nomogram for predicting pulmonary complications in elderly patients undergoing thoracic surgery

BackgroundPostoperative pulmonary complications (PPCs) remain a prevalent concern among elderly patients undergoing surgery, with a notably higher incidence observed in elderly patients undergoing thoracic surgery. This study aimed to develop a nomogram to predict the risk of PPCs in this population.MethodsA total of 2963 elderly patients who underwent thoracic surgery were enrolled and randomly divided into a training cohort (80%, n = 2369) or a validation cohort (20%, n = 593). Univariate and multivariate logistic regression analyses were conducted to identify risk factors for PPCs, and a nomogram was developed based on the findings from the training cohort. The validation cohort was used to validate the model. The predictive accuracy of the model was evaluated by receiver operating characteristic (ROC) curve, area under ROC (AUC), calibration curve, and decision curve analysis (DCA).ResultsA total of 918 (31.0%) patients reported PPCs. Nine independent risk factors for PPCs were identified: preoperative presence of chronic obstructive pulmonary disease (COPD), elevated leukocyte count, higher partial pressure of arterial carbon dioxide (PaCO2) level, surgical site, thoracotomy, intraoperative hypotension, blood loss > 100 mL, surgery duration > 180 min, and malignant tumor. The AUC value for the training cohort was 0.739 (95% CI: 0.719–0.762), and it was 0.703 for the validation cohort (95% CI: 0.657–0.749). The P-values for the Hosmer-Lemeshow test were 0.633 and 0.144 for the training and validation cohorts, respectively, indicating a notable calibration curve fit. The DCA curve indicated that the nomogram could be applied clinically if the risk threshold was between 12% and 84%, which was found to be between 8% and 82% in the validation cohort.ConclusionThis study highlighted the pressing need for early detection of PPCs in elderly patients undergoing thoracic surgery. The nomogram exhibited promising predictive efficacy for PPCs in elderly patients undergoing thoracic surgery, enabling the identification of high-risk patients and consequently aiding in the implementation of preventive interventions.Graphical