Lowest Akaike Information Criterion Value Research Articles

Comparative analysis of adolescent hypertension definitions for predicting early adulthood carotid artery intima-media thickness: Tehran lipid and glucose study.

Definitions of childhood and adolescent hypertension (HTN) do not precisely elucidate the relationship between HTN and cardiovascular outcomes. Carotid intima-media thickness (CIMT), as a substitute for cardiovascular outcomes, enables the early identification of cardiovascular events throughout early adulthood. This study aimed to compare the ability of childhood HTN definitions to predict a high CIMT in early adulthood. This prospective cohort study included 921 individuals aged 10-17 years from the Tehran Lipid and Glucose Study. The CIMT was measured after 18 years of follow-up. Participants were categorized into normal blood pressure (BP), high-normal BP, HTN stage 1, and HTN stage 2 groups based on the childhood HTN definitions of the 4th report, European Society of Hypertension (ESH), and American Academy of Pediatrics Clinical Practice Guidelines (AAP-CPG). Akaike information criterion (AIC) and relative efficiencies (RE) were calculated to compare the ability of each to predict a high CIMT (≥95th percentile) during early adulthood. The highest and lowest prevalence of stage 1 HTN was observed with the AAP-CPG (17.7%) and ESH (8.8%), respectively. Similarly, the highest and lowest prevalence of stage 2 HTN was noted with the AAP-CPG (1.5%) and ESH (0.8%), respectively. According to the RE values, the highest to lowest predictive abilities belonged to the 4th report, ESH, and AAP-CPG, respectively. In all models, the 4th report's pediatric HTN definition had the lowest AIC value and offered the best predictive ability. Among the various definitions of pediatric HTN, the 4th report offered the best ability to predict a high CIMT during early adulthood, followed by the ESH and AAP-CPG. Because the reference population of the 4th report includes overweight, obese, and normal- weight individuals, our findings suggest that excessive adiposity is among the main predictors of early adulthood atherosclerosis risk.

Pulsatile Vascular Afterload Measurement Improves Prediction of Right-Sided Heart Dysfunction in Undifferentiated and Left-Sided Pulmonary Hypertension.

The association between raised pulmonary artery pressures, pulmonary vascular resistance (PVR), and right-sided heart dysfunction is well recognized. The added value of pulsatile indices, specifically pulmonary arterial compliance (PAC) and characteristic impedance, remains unclear. This study aimed to identify the optimal vascular afterload model for predicting right-sided heart dysfunction in newly diagnosed pulmonary hypertension (PH). This was a prospective cohort study of 86 patients with suspected PH (60±16 years; 66 women) who underwent right-sided heart catheterization and cardiac magnetic resonance imaging. Right ventricular end-diastolic volume index (RVEDVi) and right atrial (RA) volume index were measured using cardiac magnetic resonance imaging. Vascular resistance, PAC, and characteristic impedance were measured using pulmonary artery pressure from right-sided heart catheterization and pulmonary artery flow from cardiac magnetic resonance imaging. Optimal model fit was determined by the lowest Akaike Information Criterion value. A total of 58% of patients had a right ventricular ejection fraction <50%, 63% had an increased RVEDVi, and 56% had an increased RA volume index. Overall, PVR was the best predictor of right ventricular ejection fraction <50% (P<0.01), increased RVEDVi (P<0.01), and RA volume index (P=0.02). However, for patients with a PVR <240 dynes.s.cm-5, PACclinical outperformed PVR in predicting right ventricular ejection fraction <50% (P=0.02) and increased RVEDVi (P=0.05). A 3-component model (PVR, PACclinical, and characteristic impedance) best predicted RA dilatation (P=0.05). In paired analysis, alternative methods of PAC quantification better predicted increased RVEDVi (PACdecay P=0.02) and RA volume index (PACarea P=0.02). PACclinical <2.3 mL/mm Hg optimally predicted right-sided heart dysfunction (area under the curve, 0.768) and increased the AUC for PVR in undifferentiated/left-sided PH (0.89 versus 0.72). Pulsatile vascular afterload measurement improves right-sided heart dysfunction prediction in undifferentiated/left-sided PH. More accurate PAC measurement methods could further enhance prognostic accuracy, while a PVR ≥240 dynes.s.cm-5 remains a valuable threshold for precapillary PH.

Performance Assessment of Some Count Data Models to Immunization Coverage Data

This research evaluates the performance of various count data models, including Poisson Regression (PR), Zero-Inflated Poisson Regression (ZIP), Zero-Truncated Poisson Regression (ZTP), Truncated Negative Binomial Poisson Regression (TNBP), and Negative Binomial Poisson Regression (NBP), using immunization coverage data from the National Primary Health Care Development Agency (NPHCDA). The study focuses on children under 12 months, assessing model fit using Likelihood Ratio (LR), Akaike Information Criterion (AIC), and Bayesian Information Criterion (BIC) criteria. Analysis conducted with STATA indicates that the Truncated Negative Binomial Poisson Regression (TNBP) outperformed other models in fit and efficiency. Both the ZTeeP and TNBP models demonstrated the best fit, with lower AIC (1959.107) and BIC (2037.649) values and higher Pseudo R-squared values (0.0677 for ZTP and 0.0590 for TNBP), compared to standard models. Age was identified as a significant predictor, negatively associated with immunization status, implying that older infants in the under-12-month category are less likely to receive all vaccinations. The ZTP model showed significant positive effects for antigens such as HepB0, OPV0, BCG, and Measles, with age having a significant negative association. The findings highlight the importance of selecting appropriate statistical models for accurate public health data analysis, enhancing decision-making in immunization programs.

Effect of chemical modification on hydraulic conductivity of stratified porous media

ABSTRACT Understanding hydraulic conductivity is essential in porous media as it dictates the capacity of fluids to permeate through these substances. This research investigated the impact of chemical modification using fly ash on hydraulic conductivity. The coefficient of hydraulic conductivity along the bedding plane at different angles of orientations was predicted by employing machine learning techniques such as gene expression programming (GEP), artificial neural networks (ANNs), and regression tree (RT), considering various influencing parameters. As the amount of fly ash in the porous medium rises, the hydraulic conductivity decreases. The statistical results demonstrate that the proposed ANN model effectively forecasted the horizontal hydraulic conductivity. It achieved the highest R2 value (0.989), the lowest mean absolute percentage error value (5.772), the lowest scatter index value (0.040), and the lowest Akaike Information Criterion value (−2,359.914) compared to the GEP, RT, and previously established approaches. Moreover, a novel equation has been proposed for determining horizontal hydraulic conductivity, which can be used for any stratified medium, distinguishing this work from previous ones. These findings can inform global practices in mitigating groundwater contamination, enhancing irrigation efficiency, and improving the design of subsurface fluid flow systems, making it a valuable contribution to both academic and practical applications worldwide.

Assessment of heart rate measurements by commercial wearable fitness trackers for early identification of metabolic syndrome risk

Metabolic syndrome increases the risks of cardiovascular diseases, type 2 diabetes, and certain cancers. The early detection of metabolic syndrome is clinically relevant, as it enables timely and targeted interventions. In the current study, we aimed to investigate the association between metabolic syndrome and heart rate measured using wearable devices in a real-world setting and compare this association with that for clinical resting heart rate. Data from 564 middle-aged adults who wore wearable devices for at least 7 days with a minimum daily wear time of 20 h were analyzed. The results showed significantly elevated all-day, sleeping, minimum, and inactive heart rates in men with pre-metabolic or metabolic syndrome compared with those in normal individuals, whereas sleeping heart rate and heart rate dips were significantly increased and decreased, respectively, in women with metabolic syndrome. After adjusting for confounders, every 10-beats-per-minute increment in all-day, sleeping, minimum, and inactive heart rates in men corresponded to odds ratios of 2.80 (95% confidence interval 1.53–5.44), 3.06 (1.57–6.40), 4.21 (1.87–10.47), and 3.09 (1.64–6.29), respectively, for the presence of pre-metabolic or metabolic syndrome. In women, the association was significant only for heart rate dips (odds ratio = 0.49 [95% confidence interval 0.25–0.96] for every 10% increment). Models incorporating inactive or minimum heart rate in men and heart rate dip in women demonstrated better fits, as indicated by lower Akaike information criterion values (170.3 in men and 364.9 in women), compared with models that included clinical resting heart rate (173.4 in men and 369.1 in women). These findings suggest that the heart rate indices obtained from wearable devices may facilitate early identification of metabolic syndrome.

Cosmology in f (R, T) modified gravity : unified dark matter and dark energy model constrained by current observations

The proposed cosmological model deals with modified Chaplygin gas (MCG) in f(R, T) = R + ξ(T) gravity, where R is the Ricci Scalar and T is the trace of energy-momentum tensor. The function ξ(T) is chosen as the linear combination of power law and logarithmic form under flat Friedmann-Lemaitre-Robertson-Walker space-time. The model is compatible with current observational data (Pantheon Type Ia Supernova) and confronts the deceleration and state parameters effectively. The model can predict the Big Rip in future infinity and can also tackle the difficulties related to the fine-tuning and the coincidence problem practically. Further, we have numerically solved the modified Friedmann equations in f(R, T) gravity and also performed a Markov Chain Monte Carlo analysis to obtain the best fit parameters of this current cosmological model. These best parameters are then used to compute the cosmographic parameters, i.e., the deceleration parameter, the jerk parameter and the snap parameter. Significantly, the cosmographic test has given valuable insights into the dynamics of the current cosmological model and also enriched us to understand about the cosmic evolution of the accelerated Universe. Additionally, the Statefinder diagnostics and O m diagnostics have provided deeper insights into the dynamics of the cosmic expansion and also provided information to distinguish between both the cosmological frameworks. Furthermore, these tests also reveal that at late times, the current model goes beyond the phantom region. Again, the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) have provided enough support for the current model under consideration, indicating that the present model exhibits a plausible explanation. However, the ΛCDM model has emerged with the lowest AIC value which suggests its relatively superior fit compared to the current model. Finally, our current model aligns well with several recent observations and unveils various intriguing features about the late time accelerated Universe.

Improving analysis of cognitive outcomes in cardiovascular trials using different statistical approaches

BackgroundThe Mini-Mental State Examination (MMSE) and the Montreal Cognitive Assessment (MoCA) questionnaires are commonly used to measure global cognition in clinical trials. Because these scales are discrete and bounded with ceiling and floor effects and highly skewed, their analysis as continuous outcomes presents challenges. Normality assumptions of linear regression models are usually violated, which may result in failure to detect associations with variables of interest.MethodsAlternative approaches to analyzing the results of these cognitive batteries include transformations (standardization, square root, or log transformation) of the scores in the multivariate linear regression (MLR) model, the use of nonlinear beta-binomial regression (which is not dependent on the assumption of normality), or Tobit regression, which adds a latent variable to account for bounded data. We aim to empirically compare the model performance of all proposed approaches using four large randomized controlled trials (ORIGIN, TRANSCEND, COMPASS, and NAVIGATE-ESUS), and using as metrics the Akaike information criterion (AIC). We also compared the treatment effects for the methods that have the same unit of measure (i.e., untransformed MLR, beta-binomial, and Tobit).ResultsThe beta-binomial consistently demonstrated superior model performance, with the lowest AIC values among nearly all the approaches considered, followed by the MLR with square root and log transformations across all four studies. Notably, in ORIGIN, a substantial AIC reduction was observed when comparing the untransformed MLR to the beta-binomial, whereas other studies had relatively small AIC reductions. The beta-binomial model also resulted in a significant treatment effect in ORIGIN, while the untransformed MLR and Tobit regression showed no significance. The other three studies had similar and insignificant treatment effects among the three approaches.ConclusionWhen analyzing discrete and bounded outcomes, such as cognitive scores, as continuous variables, a beta-binomial regression model improves model performance, avoids spurious significance, and allows for a direct interpretation of the actual cognitive measure.Trials registrationORIGIN (NCT00069784). Registered on October 1, 2003; TRANSCEND (NCT00153101). Registered on September 9, 2005; COMPASS (NCT01776424). Registered on January 24, 2013; NAVIGATE-ESUS (NCT02313909). Registered on December 8, 2014.

Evaluation of lymph node ratios and log odds of positive nodes as prognostic indicators in primary organ malignancy

BackgroundStaging of malignant tumours based on positive lymph node number and not taking into account the actual number of examined lymph nodes during surgery may lead to bias and fallacious results. The ratio of positive and resected lymph nodes (LNR) and log odds of positive nodes (LODDS) emerged as novel prognostic markers in deciding the future course of management better than the parameter pN in predicting cancer-specific survival. We aim to evaluate and compare the prognostic value of pN, LNR and LODDS in common primary organ malignancies like breast, head and neck, gastric and cervical cancers. MethodsThis was an ambidirectional observational study conducted from September 2018 to August 2021 with one year (from 2017 to 2018) for retrospective data collection archived from records. It included patients affected by breast, head and neck, gastric and cervical cancers and were followed up for a period of 3 years from the date of diagnosis to perform a holistic survival analysis. The sample was grouped by American Joint Committee on Cancer N stage, LNR and LODDS, and analysed using Kaplan–Meier and multivariate Cox proportional hazard models. ResultsThe study included 202 breast cancer patients, 85 head and neck cancer patients, 100 gastric cancer patients and 52 cervical cancer patients. Multivariate Cox regressions analysis demonstrated a lower Akaike Information Criterion (AIC) value for LODDS and LNR followed by pN stage. ConclusionThe novel indicators LNR and LODDS better predicted the biological behaviour irrespective of lymph node number and allowed clinicians to correctly stratify patients for better postoperative management.

A Combined OCBA–AIC Method for Stochastic Variable Selection in Data Envelopment Analysis

This study introduces a novel approach to enhance variable selection in Data Envelopment Analysis (DEA), especially in stochastic environments where efficiency estimation is inherently complex. To address these challenges, we propose a game cross-DEA model to refine efficiency estimation. Additionally, we integrate the Akaike Information Criterion (AIC) with the Optimal Computing Budget Allocation (OCBA) technique, creating a hybrid method named OCBA–AIC. This innovative method efficiently allocates computational resources for stochastic variable selection. Our numerical analysis indicates that OCBA–AIC surpasses existing methods, achieving a lower AIC value. We also present two real-world case studies that demonstrate the effectiveness of our approach in ranking suppliers and tourism companies under uncertainty by selecting the most suitable partners. This research enriches the understanding of efficiency measurement in DEA and makes a substantial contribution to the field of performance management and decision-making in stochastic contexts.

Baseline 18F-FDG PET/CT radiomics for prognosis prediction in diffuse large B cell lymphoma with extranodal involvement.

The objective of this investigation is to explore the capability of baseline 18F-FDG PET/CT radiomics to predict the prognosis of diffuse large B-cell lymphoma (DLBCL) with extranodal involvement (ENI). 126 patients diagnosed with DLBCL with ENI were included in the cohort. The least absolute shrinkage and selection operator (LASSO) Cox regression was utilized to refine the optimum subset from the 1328 features. Cox regression analyses were employed to discern significant clinical variables and conventional PET parameters, which were then employed with radiomics score to develop combined model for predicting both progression-free survival (PFS) and overall survival (OS). The fitness and the predictive capability of the models were assessed via the Akaike information criterion (AIC) and concordance index (C-index). 62 patients experienced disease recurrence or progression and 28 patients ultimately died. The combined model exhibited a lower AIC value compared to the radiomics model and SDmax/clinical variables for both PFS (507.101 vs. 510.658 vs. 525.506) and OS (215.667 vs. 230.556 vs. 219.313), respectively. The C-indices of the combined model, radiomics model, and SDmax/clinical variables were 0.724, 0.704, and 0.615 for PFS, and 0.842, 0.744, and 0.792 for OS, respectively. Kaplan--Meier curves showed significantly higher rates of relapse and mortality among patients classified as high-risk compared to those classified as low-risk (all P < 0.05). The combined model of clinical variables, conventional PET parameters, and baseline PET/CT radiomics features demonstrates a higher accuracy in predicting the prognosis of DLBCL with ENI.

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.

SQEAIR: an Improved Infectious Disease Dynamics Model

The Covid-19 pandemic poses a significant threat to human health and life. Timely and accurate prediction of the epidemic’s trajectory is crucial for devising effective prevention and control strategies. Traditional infectious disease models may not capture the complexity of modern epidemics, especially when governments implement diverse policies. Drawing from China’s epidemic prevention strategies and Covid-19 transmission characteristics, this study introduces two distinct categories quarantined cases and asymptomatic cases to enhance the traditional SEIR model in depicting disease dynamics. To address the intricate nature of prevention and control efforts, the quarantined cases are further segmented into three subgroups: exposed quarantined, asymptomatic quarantined, and infected quarantined cases. Consequently, a novel SQEAIR model is proposed to model the dynamics of Covid-19. Evaluation metrics such as the Akaike information criterion (AIC) and Absolute Percentage Error (MAE) are employed to assess the efficacy and accuracy of both the newly proposed and traditional models. By fitting the models to the number of infected cases in Shanghai (March to May 2022) and Guangzhou (November 2022), it was observed that the SQEAIR model exhibited a lower AIC value compared to the SEIR model, indicating superior fitting accuracy for Covid-19 infections. Moreover, the high accuracy of the SQEAIR model enabled precise predictions of confirmed cases in Guangzhou. Leveraging the SQEAIR model, various parameters were tested to simulate the impact of different influencing factors, enabling the evaluation of defense strategies. These findings underscore the effectiveness of key epidemic control measures, such as quarantining exposed cases, in enhancing public health and promoting awareness of personal protection.

Conventional and machine learning-based risk scores for patients with early-stage hepatocellular carcinoma.

The performance of machine learning (ML) in predicting the outcomes of patients with hepatocellular carcinoma (HCC) remains uncertain. We aimed to develop risk scores using conventional methods and ML to categorize early-stage HCC patients into distinct prognostic groups. The study retrospectively enrolled 1,411 consecutive treatment-naïve patients with the Barcelona Clinic Liver Cancer (BCLC) stage 0 to A HCC from 2012 to 2021. The patients were randomly divided into a training cohort (n=988) and validation cohort (n=423). Two risk scores (CATS-IF and CATS-INF) were developed to predict overall survival (OS) in the training cohort using the conventional methods (Cox proportional hazards model) and ML-based methods (LASSO Cox regression), respectively. They were then validated and compared in the validation cohort. In the training cohort, factors for the CATS-IF score were selected by the conventional method, including age, curative treatment, single large HCC, serum creatinine and alpha-fetoprotein levels, fibrosis-4 score, lymphocyte-tomonocyte ratio, and albumin-bilirubin grade. The CATS-INF score, determined by ML-based methods, included the above factors and two additional ones (aspartate aminotransferase and prognostic nutritional index). In the validation cohort, both CATS-IF score and CATS-INF score outperformed other modern prognostic scores in predicting OS, with the CATSINF score having the lowest Akaike information criterion value. A calibration plot exhibited good correlation between predicted and observed outcomes for both scores. Both the conventional Cox-based CATS-IF score and ML-based CATS-INF score effectively stratified patients with early-stage HCC into distinct prognostic groups, with the CATS-INF score showing slightly superior performance.

Application of Mixture and Non-mixture Cure Models in Survival Analysis of Patients With COVID-19.

Background Due to the emergence of new COVID-19 mutations and an increase in re-infection rates, it has become an important priority for the medical community to identify the factors affecting the short- and long-term survival of patients. This study aimed to determine the risk factors of short- and long-term survival in patients with COVID-19 based on mixture and non-mixture cure models. Methodology In this study, the data of 880 patients with COVID-19 confirmed with polymerase chain reaction in Fereydunshahr city (Isfahan, Iran) from February 20, 2020, to December 21, 2021, were gathered, and the vital status of these patients was followed for at least one year. Due to the high rate of censoring, mixture and non-mixture cure models were applied to estimate the survival. Akaike information criterion values were used to evaluate the fit of the models. Results In this study, the mean age of the patients was 48.9 ± 21.23 years, and the estimated survival rates on the first day, the fourth day, the first week, the first month, and at one year were 0.997, 0.982, 0.973, 0.936, and 0.928, respectively. Among the parametric models, the log-logistic mixed cure model with the logit link, which showed the lowest Akaike information criterion value, demonstrated the best fit. The variables of age and prescribed medication type were significant predictors of long-term survival, while occupation was influential in the short-term survival of patients. Conclusions According to the results of this study, it can be concluded that elderly patients should observe health protocols more strictly and consider receiving booster vaccine doses. The log-logistic cure model with a logit link can be used for survival analysis in COVID-19 patients, a fraction of whom have long-term survival.

Relationship Between Tenofovir Diphosphate Concentrations in Dried Blood Spots and Virological Outcomes After Initiating Tenofovir-Lamivudine-Dolutegravir as First-Line or Second-Line Antiretroviral Therapy.

Tenofovir diphosphate (TFV-DP) concentration in dried blood spots is a marker of long-term adherence. We investigated the relationship between TFV-DP concentrations and virological outcomes in participants initiating tenofovir-lamivudine-dolutegravir (TLD) as first-line or second-line antiretroviral therapy. Three primary care clinics in Khayelitsha, Cape Town, South Africa. We conducted a post hoc analysis of 2 randomized controlled trials of participants initiating TLD. TFV-DP concentrations and viral loads were measured at 12, 24, and 48 weeks. Multivariable logistic regression was performed to assess the association with virological suppression (<50 copies/mL) per natural logarithm increase in TFV-DP concentration. Generalized estimating equations with logit link were used to assess associations with virological rebound. The Akaike Information Criterion and Quasi-likelihood Information Criteria were used to compare models built on continuous TFV-DP data to 4 previously defined concentration categories. We included 294 participants in the analysis, 188 (64%) of whom initiated TLD as second-line therapy. Adjusted odds ratios (95% CIs) of virological suppression were 2.12 (1.23, 3.75), 3.11 (1.84, 5.65), and 4.69 (2.81, 8.68) per natural logarithm increase in TFV-DP concentration at weeks 12, 24, and 48, respectively. In participants with virological suppression at week 12, the adjusted odds ratio for remaining virologically suppressed was 3.63 (95% CI: 2.21 to 5.69) per natural logarithm increase in TFV-DP concentration. Models using continuous TFV-DP data had lower Akaike Information Criterion and Quasi-likelihood Information Criteria values than those using categorical data for predicting virological outcomes. TFV-DP concentrations in dried blood spots exhibit a dose-response relationship with viral load. Analyzing TFV-DP concentrations as continuous variables rather than conventional categorization may be appropriate.

Prediction of Ki-67 expression in bladder cancer based on CT radiomics nomogram.

This study aimed to create and validate a radiomics nomogram for non-invasive preoperative Ki-67 expression level prediction in patients with bladder cancer (BCa) using contrast-enhanced CT radiomics features. A retrospective analysis of 135 patients was conducted, 79 of whom had high levels of Ki-67 expression and 56 of whom had low levels. For the dimensionality reduction analysis, the best features were chosen using the least absolute shrinkage selection operator and one-way analysis of variance. Then, a radiomics nomogram was created using multiple logistic regression analysis based on radiomics features and clinical independent risk factors. The performance of the model was assessed using the Akaike information criterion (AIC) value, the area under the curve (AUC) value, accuracy, sensitivity, and specificity. The clinical usefulness of the model was assessed using decision curve analysis (DCA). Finally, to establish a radiomics nomogram, the best 5 features were chosen and integrated with the independent clinical risk factors (T stage) and Rad-score. This radiomics nomogram demonstrated significant correction and discriminating performance in both the training and validation sets, with an AUC of 0.836 and 0.887, respectively. This radiomics nomogram had the lowest AIC value (AIC = 103.16), which was considered to be the best model. When compared to clinical factor model and radiomics signature, DCA demonstrated the more value of the radiomics nomogram. Enhanced CT-based radiomics nomogram can better predict Ki-67 expression in BCa patients and can be used for prognosis assessment and clinical decision making.

Development and evaluation of the Newstage system: integrating tumor regression grade and lymph node status for improved prognostication in neoadjuvant treatment of gastric cancer

BackgroundThe predictive correlation of tumor depth of invasion changes after neoadjuvant therapy, and the 8th American Joint Committee on Cancer (AJCC) ypTNM system for gastric cancer may not accurately predict patient prognosis following neoadjuvant therapy.MethodsA retrospective analysis was conducted on a total of 258 patients who underwent radical surgery for gastric cancer after neoadjuvant therapy. The Newstage system was established based on tumor regression grade and pathological lymph node status. The 3-year survival rates of patients classified by the Newstage system were compared with those classified by the AJCC ypTNM system.ResultsIn a cohort of 258 patients, the 3-year overall survival rates based on the Newstage system were: (I) 94.6%, (II) 79.3%, (III) 54.5%, and (IV) 30.2%. The Newstage system exhibited a lower Akaike information criterion value (902.57 vs. 912.03). Additionally, the area under the ROC curve (0.756 vs. 0.733) and the C-index (0.731 vs. 0.718) was higher than the AJCC ypTNM system. Furthermore, a multivariate analysis indicated that the Newstage system was an independent prognostic factor (p = 0.001).ConclusionThe Newstage system exhibits superior predictive performance in estimating survival rates for neoadjuvant therapy in gastric cancer. It also functions as an independent prognostic factor.

Anterior Knee Pain Scale (AKPS): structural and criterion validity in Brazilian population with patellofemoral pain

PurposeTo identify the best internal structure of the Brazilian version of the Anterior Knee Pain Scale (AKPS), comparing different instrument structures (structural validity) and correlating the scores of the versions (criterion validity).MethodsWe included Brazilian volunteers, aged ≥ 18 years, with patellofemoral pain (PFP) for at least 3 months. We used the confirmatory factor analysis and considered the following fit indices: chi-square/degrees of freedom (DF), comparative fit index (CFI), Tucker-Lewis index (TLI), root mean square error of approximation (RMSEA). We considered the structure with the lowest values of the Akaike information criterion (AIC), sample size adjusted Bayesian information criterion (SABIC), and assessed criterion validity using Pearson correlation coefficient (r) to correlate the long and short versions.ResultsThe study included 101 participants, mostly women (65.3%), young adults (~ 31 years old), overweight (BMI > 25 kg/m2), incomplete higher education (37.6%), and physically active (64.4%). The original 1-domain, 13-item structure showed adequate fit indices (chi-square/GL < 3.00, TLI and CFI > 0.90, and RMSEA < 0, 08). However, items 11 and 12 had a factorial load of less than 0.23. Therefore, we excluded items 11 and 12 and found adequate fit indices (chi-square/GL < 3.00, TLI and CFI > 0.90, and RMSEA < 0, 08) and lower AIC and SABIC values. We observed a correlation coefficient above the acceptable cutoff of 0.70 (r = 0.966, p-value < 0.001) between the versions.ConclusionThe 11-item AKPS (without items 11 and 12) is the version with the most adequate internal structure and correlates satisfactorily with the long version of the instrument.

Unveiling Recurrence Patterns: Analyzing Predictive Risk Factors for Breast Cancer Recurrence after Surgery.

Breast cancer (BC) stands as the second-leading cause of female-specific cancer-related fatalities globally, necessitating comprehensive research to address its critical aspects. This study aimed to explore the time intervals between surgery and disease recurrence in BC patients and their survival utilizing various parametric and semi-parametric models. After the examination of data collected from 2010 to 2021 at a BC Center in Tehran, Iran, 171 cases met the criteria for analysis out of 2246 datasets. Model fitting, was assessed through the Akaike Information Criterion (AIC), and indicated the logistic distribution as the most fit one among concurrent and independent variable models. The Cox proportional hazard regression model consistently demonstrated superior fitting, characterized by the lowest AIC values. The average age at diagnosis was 50.39 years, with a standard deviation of 11.13. Typical survival time was estimated 53.44 months, falling within a confidence interval of 51.41-55.48 months at a 95% confidence level. The 1-year survival rate was determined at 0.92 (95% CI: 0.89-0.94). Notably, patient age while cancer diagnosis, progesterone receptor (PR), tumor grade, and tumor stage were found to be statistically significant (P < .05) risk factors for prediction of BC recurrence after surgery in Iran by Cox model. Our findings underscore the importance of further exploration and consideration of the identified risk factors in BC research and treatment strategies.

Perhitungan Premi Murni Kendaraan Bermotor Menggunakan Generalized Linear Models

Insurance is a form of agreement between the insurer against the insured party (insurance company), where the insurer will pay a premium to the insured every month as a form of collateral for loss or damage that befalls him for certain events. Researchers took data by distributing questionnaires to respondents. Some covariate variables used in the study include: gender, vehicle age, vehicle insurance costs, vehicle use, vehicle mileage every year, and claims records in the last three years. Meanwhile, the dependent variable is frequency claim and claim severity. The researcher estimates the Generalized Linear Models (GLZ) parameters using the maximum likelihood estimation (MLE). The next step, researchers conducted a model compatibility test on the dependent variable using calculations on the Akaike Information Criteria (AIC), where the model chosen was the model with the lowest AIC value. Based on this, the researcher can determine covariate variables that affect the dependent variable and model it. The final step is the researcher calculates the probability of claim frequency, claim severity, and estimates the amount of pure premiums charged to respondents.