Use Of Predictive Models Research Articles

The Role of Predictive Analytics in Disease Prevention : A Technical Overview

This article explores the transformative potential of predictive analytics in healthcare, focusing on its applications in disease prevention and public health management. It examines the power of data integration from diverse sources, the use of predictive modeling for risk stratification, and the broader implications for public health surveillance and chronic disease management. The article also discusses the significant growth of AI in the healthcare market and highlights successful implementations of predictive analytics across various medical domains. Additionally, it addresses the key challenges in implementing these technologies, including data privacy concerns, integration issues, model accuracy, and ethical considerations. Through numerous case studies and statistical evidence, the article demonstrates how predictive analytics is revolutionizing healthcare by enabling more accurate, personalized, and proactive approaches to disease prevention and management.

PREDICTIVE TECHNIQUES AND ARTIFICIAL INTELLIGENCE MODELS IN THE MANAGEMENT OF DEFAULTY PUBLIC DEBTS: COMPARISON BETWEEN LINEAR REGRESSION AND DECISION TREES

The study analyzed focuses on the application of predictive models, specifically Linear Regression and Decision Trees, for the management of defaulted debts in the public context of the United States. The main objective of the work is to compare the effectiveness of these models in predicting the compliance of debts with more than 120 days, assisting in directing these debts to the Treasury Offset Program (TOP), an essential initiative for the government's financial recovery. The problem that the study addresses is the need for effective management of defaulted public debts, seeking to ensure compliance with public financial policies that promote compliance and the adequate redirection of financial resources to the government. This is particularly important to ensure fiscal transparency and accountability of federal agencies. The methodology used in the study was quantitative, based on the analysis of data on eligible debts extracted from reports of the US Treasury. Linear Regression and Decision Tree models were applied, with performance metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE) and Coefficient of Determination (R²). The study addressed financial and temporal variables to analyze the behavior of these debts and their compliance. The main results show that both models presented high accuracy in predictions, with Linear Regression showing a perfect fit (R² = 1) and Decision Trees standing out in capturing non-linear nuances of the data. The variable "Compliance Rate Amount" was identified as the most significant in the Decision Tree model, suggesting that the amount of the compliance rate is one of the most important factors in predicting the compliance of defaulted debts. This study offers valuable contributions to the field of public management, by demonstrating that the use of predictive models can help optimize debt recovery, improve fiscal transparency and contribute to more informed decision-making.

Use of Predictive Models to Determine Transplant Eligibility

Use of Predictive Models to Determine Transplant Eligibility

The use of predictive modelling to determine the likelihood of donor return during the COVID-19 pandemic.

Artificial intelligence (AI) uses sophisticated algorithms to "learn" from large volumes of data. This could be used to optimise recruitment of blood donors through predictive modelling of future blood supply, based on previous donation and transfusion demand. We sought to assess utilisation of predictive modelling and AI blood establishments (BE) and conducted predictive modelling to illustrate its use. A BE survey of data modelling and AI was disseminated to the International Society of Blood transfusion members. Additional anonymzed data were obtained from Italy, Singapore and the United States (US) to build predictive models for each region, using January 2018 through August 2019 data to determine likelihood of donation within a prescribed number of months. Donations were from March 2020 to June 2021. Ninety ISBT members responded to the survey. Predictive modelling was used by 33 (36.7%) respondents and 12 (13.3%) reported AI use. Forty-four (48.9%) indicated their institutions do not utilise predictive modelling nor AI to predict transfusion demand or optimise donor recruitment. In the predictive modelling case study involving three sites, the most important variable for predicting donor return was number of previous donations for Italy and the US, and donation frequency for Singapore. Donation rates declined in each region during COVID-19. Throughout the observation period the predictive model was able to consistently identify those individuals who were most likely to return to donate blood. The majority of BE do not use predictive modelling and AI. The effectiveness of predictive model in determining likelihood of donor return was validated; implementation of this method could prove useful for BE operations.

Risk Prediction Models for Adverse Drug Reactions and Adverse Drug Events in Older Adults – A Systematic Review and Meta-analysis

Abstract Background Adverse drug reactions (ADRs) and adverse drug events (ADEs) are common and result in significant morbidity, mortality and healthcare costs. As the world’s population ages, the consumption of medications and healthcare will increase. Models predicting ADR risks in older adults have previously been found to lack reliability and validity. The aim of this systematic review and meta-analysis is to provide an updated, comprehensive quality assessment and analysis of ADR-risk prediction tools in older adults. Methods Standard computerised-databases and citations were searched (2012 to 2023) to identify relevant peer-reviewed studies. Studies which developed and/or validated an ADR/ADE prediction model for use in older adults were included. Four studies from a previous systematic review were also included. The TRIPOD (transparent reporting of a multivariable prediction model for individual prognosis or diagnosis) guidelines were used to evaluate each of the included studies. Random effects models were used to derive a pooled discrimination estimate (area under the receiver operation curve; AUROC) for model development studies and model validation studies. The prediction model risk of bias assessment tool (PROBAST) was applied to evaluate bias. Results Five out of 11,481 titles, plus four previous studies, met all inclusion criteria and underwent TRIPOD evaluation. Meta-analysis resulted in a favourable pooled AUROC 0.75 (95% CI 0.57, 0.87; I-squared= 96.65%) for model development studies and 0.70 (95% CI 0.57, 0.80; I-squared= 90.53%) for model validation studies but there was substantial heterogeneity. Studies had poor adherence (range 32-50%; median 44%; IQR 11.5%) to TRIPOD guidelines. The overall risk of bias was high for all included studies. Conclusion Seven risk prediction models were identified, all exhibiting poor adherence to TRIPOD guidelines which may question the investigational rigor of the studies and their usability. This underscores the need for the development of a validated, robust and reliable tool worthy of implementation and testing in a real-world setting.

Model ensemble techniques of machine learning algorithms for soil moisture constants in the semi‐arid climate conditions

AbstractIn recent years, the use of prediction models based on intelligent algorithms has become widespread in soil science. However, each algorithm has advantages and disadvantages, and variable results can occur on different datasets. The evaluation of ensemble techniques for solving these problems is the current approach. Water problems will arise due to global warming, and soil water will become more important. This study aims to evaluate the predictive accuracy of different machine learning algorithms (support vector machine regression (SVR), random forest (RF), artificial neural network (ANN), and multivariate linear regression (MLR)) and ensemble techniques (equal weight [EQ], Bates–Granger‐BG), Granger–Ramanathan (GR), Akaike information criterion (AIC), and Bayesian information criterion (BIC)) on the field capacity (FC), wilting point (WP) and available water content (AWC) of soils. As a result, higher prediction accuracy was obtained with the RF algorithm than with the value machine learning algorithm in the estimation of moisture constants. The coefficients of determination (R2) obtained for the prediction of FC, WP, and AWC via the RF algorithms were 0.624, 0.759 and 0.641, respectively. MLR had the highest error rate. Among the ensemble techniques, GR was the most successful. Lin's concordance correlation coefficient (LCCC) values obtained from the estimation of FC, WP, and AWC with the GR model were 0.801, 0.894, and 0.801, respectively. The root mean squared error (RMSE) and mean absolute error (MAE) values obtained in the estimation of the available water content with the MLR algorithm were 1.905 and 1.435, respectively, whereas these values were 1.173 and 0.767, respectively, when the GR model was used. As a result of the present study, better predictive results were obtained with ensemble techniques instead of evaluating the algorithms individually.

Exploring the Role of HR Analytics in Enhancing Talent Acquisition Strategies

Hiring and selecting the right candidate is a critical business process in today’s cutthroat competition. HR Analytics, also known as Human Resource Analytics, provides unique insights into talent acquisition and helps organisations to improve their strategies based on fact. This research paper aims to establish the use of the HR Analytics in improving the talent acquisition practices since this will help in identifying the different candidates sourcing, screening, selection, and onboarding processes. In doing so, the work employs the study of different techniques of HR Analytics including but not limited to the predictive analytics, data mining, and talent market analysis with the main view of understanding how different techniques can be used to give the organizations competitive advantage in talent management and attraction. The research will focus on a few real-world solutions such as the use of HR Analytics for social media talent acquisition, the use of prediction models for screening and the use of data analytics for selection. Also, the paper will discuss the problems and drawbacks that may be encountered while adopting HR Analytics in recruiting. In it, we will highlight the data quality issue, the privacy issue, and the need that HR professionals must have skills and knowledge to use the HR Analytics tools. Lastly, this research aims at contributing to the existing body of knowledge that seeks to help organizations that aim at increasing on their Strategic Human Resources Management Talent Acquisition approach based on the design and use of HR Analytics. Day by day organizations are becoming more rational and by using this technique organizations can arrive to better decisions about employer branding and, consequently, about attracting and retaining the best talent in today’s job market.

An in vitro and machine learning framework for quantifying serum albumin binding of per- and polyfluoroalkyl substances

Abstract Per- and polyfluoroalkyl substances (PFAS) are a diverse class of anthropogenic chemicals; many are persistent, bioaccumulative, and mobile in the environment. Worldwide, PFAS bioaccumulation causes serious adverse health impacts, yet the physiochemical determinants of bioaccumulation and toxicity for most PFAS are not well understood, largely due to experimental data deficiencies. As most PFAS are proteinophilic, protein binding is a critical parameter for predicting PFAS bioaccumulation and toxicity. Among these proteins, human serum albumin (HSA) is the predominant blood transport protein for many PFAS. We previously demonstrated the utility of an in vitro differential scanning fluorimetry assay for determining relative HSA binding affinities for 24 PFAS. Here, we report HSA affinities for 65 structurally diverse PFAS from 20 chemical classes. We leverage these experimental data, and chemical/molecular descriptors of PFAS, to build 7 machine learning classifier algorithms and 9 regression algorithms, and evaluate their performance to identify the best predictive binding models. Evaluation of model accuracy revealed that the top performing classifier model, logistic regression, had an AUROC statistic of 0.936. The top performing regression model, support vector regression, had an R2 of 0.854. These top performing models were then used to predict HSA-PFAS binding for chemicals in the EPAPFASINV list of 430 PFAS. These developed in vitro and in silico methodologies represent a high-throughput framework for predicting protein-PFAS binding based on empirical data, and generate directly comparable binding data of potential use in predictive modeling of PFAS bioaccumulation and other toxicokinetic endpoints.

Force Fields, Quantum-Mechanical- and Molecular-Dynamics-Based Descriptors of Radiometal-Chelator Complexes.

Radiopharmaceuticals are currently a key tool in cancer diagnosis and therapy. Metal-based radiopharmaceuticals are characterized by a radiometal-chelator moiety linked to a bio-vector that binds the biological target (e.g., a protein overexpressed in a particular tumor). The right match between radiometal and chelator influences the stability of the complex and the drug's efficacy. Therefore, the coupling of the radioactive element to the correct chelator requires consideration of several features of the radiometal, such as its oxidation state, ionic radius, and coordination geometry. In this work, we systematically investigated about 120 radiometal-chelator complexes taken from the Cambridge Structural Database. We considered 25 radiometals and about 30 chelators, featuring both cyclic and acyclic geometries. We used quantum mechanics methods at the density functional theoretical level to generate the general AMBER force field parameters and to perform 1 µs-long all-atom molecular dynamics simulations in explicit water solution. From these calculations, we extracted several key molecular descriptors accounting for both electronic- and dynamical-based properties. The whole workflow was carefully validated, and selected test-cases were investigated in detail. Molecular descriptors and force field parameters for the complexes considered in this study are made freely available, thus enabling their use in predictive models, molecular modelling, and molecular dynamics investigations of the interaction of compounds with macromolecular targets. Our work provides new insights in understanding the properties of radiometal-chelator complexes, with a direct impact for rational drug design of this important class of drugs.

Artificial Intelligence in Pain Management: Advancing Translational Science in Digital Health Research from Bench to Bedside

Artificial Intelligence (AI) is rapidly transforming the landscape of healthcare, with particularly profound implications in the field of pain management. This chapter delves into the integration of AI-driven tools that revolutionize the way pain is assessed, monitored, and treated. Through the use of predictive modeling, real-time monitoring, and personalized treatment plans, AI significantly enhances the precision, efficiency, and effectiveness of pain management strategies. The discussion extends to various AI applications, shedding light on the ethical considerations that accompany these technological advancements, as well as outlining future research directions. Collectively, these insights underscore the immense potential of AI to not only improve pain management practices but also to significantly elevate patient outcomes. Central to this transformation is the role of translational science in bridging the gap between theoretical AI models and their practical, clinical applications. This "bench to bedside" approach ensures that innovations in AI are not merely confined to research environments but are actively integrated into real-world patient care. For instance, AI-powered predictive analytics in pain management, driven by sophisticated machine learning algorithms, have progressed from computational experiments to clinical trials, and ultimately, to widespread implementation in healthcare settings. These AI models are now being utilized in hospitals to assess patient pain levels in real-time, predict opioid requirements, and optimize pain management protocols. This progression exemplifies how translational science is facilitating a paradigm shift in healthcare, positioning AI as an indispensable tool in modern pain management.

Predictive Analytics for Identifying High-Risk Medicare Patients: Enhancing Preventive Care

The intended purpose of this paper is to carry out an analysis of the use of predictive modeling for the early identification of high-risk Medicare patients with the aim of enhancing preventative measures. MEDI-CARE, a vital policy in the United States of America, which focuses on offering health care services to the old and people with disabilities, has been criticized over such factors as Shortcoming of Medicare hence can be attributed to factors such as the growing costs of health care and the emerging cases of chronic diseases. A new type of risk stratification: The use of predictive analytics, which incorporates ML-AI, is also in the early warning system and to monitor or follow up on the patient’s adverse health event, subsequent to which care coordination plans can be formulated. The abstract, hence, is an explanation of the roles of preventive and predictive analytics, especially in health care, with such a thing as the identification of early high-risk patients and management. They are that there is a rise in the dependence on methodologies that are data-driven for the purpose of attaining enhanced healthcare outcomes, reduction in the hospitalization quotient and the effective management of diseases which occur chronically. A few such points comprise data and information from electronic health records and other digital healthcare databases, model construction, and model application. It also addresses the various ethical pitfalls likely to arise when using the concept of predictive analytics, and these include the privacy of the patient’s data, how the collected data will be protected and filtered, as well as the prejudice that might be experienced when developing the algorithm. Current and earlier models of prediction, as well as their application in Medicare and research limitations, are described in the literature review of the article. Whereas in the methodology section, the procedures used in data collection, modeling, and validation are described, the results and discussion section put into perspective the effectiveness of the work done in enhancing Preventive care through predictive analytics. Finally, the discussion in the conclusion shall be based on the findings of the study and recommend future research directions for policy making.

Risk Factors and Predictors of Prolonged Mechanical Ventilation Following Cardiac Surgery: A Narrative Review.

The subset of patients requiring prolonged mechanical ventilation is significantly high worldwide, making it an important topic of continuous and ongoing research. Over the years, various articles have shown that there may be predictors of prolonged ventilation that could be applied in healthcare to make it more patient-centered. The available literature suggests that authors have different definitions of "prolonged" ventilation. However, most critical care units embrace caution if a patient needs mechanical ventilation for more than 48 to 72 hours. The major benefits of mechanical ventilation are an overall decrease in the work of breathing and the facilitation of relatively easier pumping from an ailing heart. An elevated risk of prolonged ventilation after cardiac surgery exists in patients with higher classes of heart failure (as classified by the New York Heart Association (NYHA) or Canadian Cardiovascular Society (CCS)), a pre-existing congenital or acquired cardiac abnormality, and patients with renal failure, to name a few. The impact on quality of life has also been widely studied; as mortality rates increase with factors like age and days dependent on ventilation. Patients undergoing prolonged ventilation constitute an administrative challenge for critical care units, highlighting how multiple patients in this bracket can overwhelm the healthcare system. The use of prediction models in this context can aid healthcare delivery tremendously. Using different predictors, we can craft tailor-made treatment options and achieve the goal of more ventilator-free days per patient.

Machine learning based prospect targeting: A case of gold occurrence in central parts of Tanzania, East Africa

Soil geochemical analyses from central Tanzania reveal significant gold (Au) values, highlighting the potential for further exploration in the region. This study employs ensemble machine learning models—XGBoost-RF, XGBoost-SVM, and XGBoost-ANN—to enhance predictions of Au distribution. Among these, the XGBoost-ANN model showed the highest accuracy during the training phase, achieving a Mean Absolute Percentage Error (MAPE) of 1.275, a Root Mean Square Error (RMSE) of 0.031, an R² of 0.999, and a Pearson Correlation Coefficient (PCC) of 0.999. However, its performance declined in the testing phase with a MAPE of 0.0668 and an RMSE of 0.2491, indicating reduced predictiveness on new data. Spatial analyses using Global and Local Moran's I tests revealed no significant global spatial autocorrelation but identified localized clusters of high and low Au concentrations. Specific areas showed significant spatial dependence, enhancing our understanding of the complex geospatial distribution of Au. These findings support the combined use of predictive modeling and spatial statistical methods to refine mineral exploration strategies, highlighting the value of advanced analytics in identifying promising exploration targets.

The use of imputation in clinical decision support systems: a cardiovascular risk management pilot vignette study among clinicians

Abstract Aims A major challenge of the use of prediction models in clinical care is missing data. Real-time imputation may alleviate this. However, to what extent clinicians accept this solution remains unknown. We aimed to assess acceptance of real-time imputation for missing patient data in a clinical decision support system (CDSS) including 10-year cardiovascular absolute risk for the individual patient. Methods and results We performed a vignette study extending an existing CDSS with the real-time imputation method joint modelling imputation (JMI). We included 17 clinicians to use the CDSS with three different vignettes, describing potential use cases (missing data, no risk estimate; imputed values, risk estimate based on imputed data; complete information). In each vignette, missing data were introduced to mimic a situation as could occur in clinical practice. Acceptance of end-users was assessed on three different axes: clinical realism, comfortableness, and added clinical value. Overall, the imputed predictor values were found to be clinically reasonable and according to the expectations. However, for binary variables, use of a probability scale to express uncertainty was deemed inconvenient. The perceived comfortableness with imputed risk prediction was low, and confidence intervals were deemed too wide for reliable decision-making. The clinicians acknowledged added value for using JMI in clinical practice when used for educational, research, or informative purposes. Conclusion Handling missing data in CDSS via JMI is useful, but more accurate imputations are needed to generate comfort in clinicians for use in routine care. Only then can CDSS create clinical value by improving decision-making.

MFCC-CNN: A patient-independent seizure prediction model.

Automatic prediction of seizures is a major goal in the field of epilepsy. However, the high variability of Electroencephalogram (EEG) signals in different patients limits the use of prediction models in clinical applications. This paper proposes a patient-independent seizure prediction model, named MFCC-CNN, to improve the generalization ability. MFCC-CNN model introduces Mel-Frequency Cepstrum Coefficients (MFCC) features and Linear Predictive Cepstral Coefficients (LPCC) features concentrated in the low frequency region, which contains more detailed information. Convolutional neural network (CNN) is used to construct a seizure prediction model. Experimental results showed that the proposed model obtained accuracy of 96 , sensitivity of 92 , specificity of 84 and F1-score of 85 for 24 cases in CNHB-MIT dataset. The overall performance of MFCC-CNN model is better than the other models. MFCC-CNN model does not need to be specifically customized for different patients. As a patient-independent seizure prediction model, it has good generalization ability.

A Scoping Review of Military Combat Casualty Data on Submassive, Massive, and Supermassive Transfusions.

Blood transfusions are common during combat casualty care, aiming to address the loss of blood volume that often accompanies severe battlefield injuries. This scoping review delves into the existing military combat casualty data to analyze the efficacy, challenges, and advances in the use of massive and super-massive transfusions in the management of critically injured warfighters. We performed a scoping review of combat-related literature published between 2006 and 2023 pertaining to massive transfusions used during combat deployments. We utilized PubMed to identify relevant studies and utilized the PRISMA-ScR Checklist to conduct the review. We identified 53 studies that met the inclusion criteria with the majority being retrospective studies from registries used by the United States, British, French, and Dutch Militaries. Most of the studies focused on transfusion ratios, the movement of blood transfusions to more forward locations, implementation of massive transfusions with different fibrinogen-to-red blood cell ratios, the addition of recombinant factor VII, and the use of predictive models for transfusion. Lastly, we identified reports of improved survival for casualties with the rapid implementation of various blood products (warm fresh whole blood, cold-stored low titer group O blood, freeze-dried plasma, and component therapy) and literature relating to pediatric casualties and submassive transfusions. Notable findings include the establishment of hemodynamic and cell blood count parameters as predictors of the requirement for massive transfusions and the association of higher fibrinogen-to-red blood cell ratios with decreased mortality. We identified 53 studies focused on blood transfusions from the Global War on Terrorism conflicts. The majority were related to transfusion ratios and the movement of blood transfusions to more forward locations. We highlight key lessons learned on the battlefield that have been translated into scientific developments and changes in civilian trauma methods.

Impact of Preprocessing Parameters in Medical Imaging-Based Radiomic Studies: A Systematic Review.

Lately, radiomic studies featuring the development of a signature to use in prediction models in diagnosis or prognosis outcomes have been increasingly published. While the results are shown to be promising, these studies still have many pitfalls and limitations. One of the main issues of these studies is that radiomic features depend on how the images are preprocessed before their computation. Since, in widely known and used software for radiomic features calculation, it is possible to set these preprocessing parameters before the calculation of the radiomic feature, there are ongoing studies assessing the stability and repeatability of radiomic features to find the most suitable preprocessing parameters for every used imaging modality. We performed a comprehensive literature search using four electronic databases: PubMed, Cochrane Library, Embase, and Scopus. Mesh terms and free text were modeled in search strategies for databases. The inclusion criteria were studies where preprocessing parameters' influence on feature values and model predictions was addressed. Records lacking information on image acquisition parameters were excluded, and any eligible studies with full-text versions were included in the review process, while conference proceedings and monographs were disregarded. We used the QUADAS-2 (Quality Assessment of Diagnostic Accuracy Studies 2) tool to investigate the risk of bias. We synthesized our data in a table divided by the imaging modalities subgroups. After applying the inclusion and exclusion criteria, we selected 43 works. This review examines the impact of preprocessing parameters on the reproducibility and reliability of radiomic features extracted from multimodality imaging (CT, MRI, CBCT, and PET/CT). Standardized preprocessing is crucial for consistent radiomic feature extraction. Key preprocessing steps include voxel resampling, normalization, and discretization, which influence feature robustness and reproducibility. In total, 44% of the included works studied the effects of an isotropic voxel resampling, and most studies opted to employ a discretization strategy. From 2021, several studies started selecting the best set of preprocessing parameters based on models' best performance. As for comparison metrics, ICC was the most used in MRI studies in 58% of the screened works. From our work, we highlighted the need to harmonize the use of preprocessing parameters and their values, especially in light of future studies of prospective studies, which are still lacking in the current literature.

A Comparative Study of Phase I and II Hepatic Microsomal Biotransformation of Phenol in Three Species of Salmonidae: Hydroquinone, Catechol, and Phenylglucuronide Formation.

The in vitro biotransformation of phenol at 11 °C was studied using pre-spawn adult rainbow (Oncorhynchus mykiss) (RBT), brook (Salvelinus fontinalis) (BKT), and lake trout (Salvelinus namaycush) (LKT) hepatic microsomal preparations. The incubations were optimized for time, cofactor concentration, pH, and microsomal protein concentration. Formation of Phase I ring-hydroxylation and Phase II glucuronidation metabolites was quantified using HPLC with dual-channel electrochemical and UV detection. The biotransformation of phenol over a range of substrate concentrations (1 to 180 mM) was quantified, and the Michaelis-Menten kinetics constants, Km and Vmax, for the formation of hydroquinone (HQ), catechol (CAT), and phenylglucuronide (PG) were calculated. Species differences were noted in the Km values for Phase I enzyme production of HQ and CAT, with the following rank order of apparent enzyme affinity for substrate: RBT > BKT = LKT. However, no apparent differences in the Km for Phase II metabolism of phenol to PG were detected. Conversely, while there were no apparent differences in Vmax between species for HQ or CAT formation, the apparent maximum capacity for PG formation was significantly less in LKT than that observed for RBT and BKT. These experiments provide a means to quantify metabolic activation and deactivation of xenobiotics in fish, to compare activation and deactivation reactions across species, and to act as a guide for future predictions of new chemical biotransformation pathways and rates in fish. These experiments provided the necessary rate and capacity (Km and Vmax) inputs that are required to parameterize a fish physiologically based toxicokinetic (PB-TK) model for a reactive chemical that is readily biotransformed, such as phenol. In the future, an extensive database of these rate and capacity parameters on important fish species for selected chemical structures will be needed to allow the effective use of predictive models for reactive, biotransformation chemicals in aquatic toxicology and environmental risk assessment.

Reliability of predictive models based on the mesiodistal and buccolingual diameters of permanent canine teeth for sex estimation in forensic contexts: A systematic review and meta-analysis

This study aimed to assess the reliability of predictive models for sex estimation based on permanent canine size. A systematic literature review was performed by following the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA). Six electronic databases were searched as the primary source of information. As a secondary source of information, a manual search was performed to identify additional relevant studies not captured in the initial search. After assessing the methodological quality and risk of bias with the Joanna Briggs Institute Critical Appraisal Tools for Systematic Reviews, the data were subjected to statistical tests for a meta-analysis of diagnostic test accuracy and Higgin’s I2 statistic to evaluate the heterogeneity between the eligible studies. The systematic search resulted in 21 studies for qualitative synthesis, and 13 of them were selected for quantitative analysis. The analysis of 25 univariate predictive models showed an estimated sensitivity of 77.2 % and specificity of 67.1 %. Meta-regression analyses were performed for dental arch, the type of diameter and dental region outcomes for these univariate predictive models. Dental arch (p = 0.029) and the dental region of measurement (p = 0.001) were significant modifiers. The analysis of 25 multivariate predictive models showed an estimated sensitivity of 82.6 % and specificity of 70.1 %. There were significant methodological limitations and substantial heterogeneity among the included studies. Based on the results, there is insufficient high-quality scientific evidence to support the safe use of predictive models based on permanent canine measurements as the exclusive method for sex estimation in forensic settings.

Comparison of body composition measures assessed by bioelectrical impedance analysis versus dual-energy X-ray absorptiometry in the United Kingdom Biobank

BackgroundBioelectrical impedance analysis (BIA) and dual-energy X-ray absorptiometry (DXA) serves as common modalities for body composition assessment. This study was aimed to evaluate the agreement between BIA and DXA measures in UK Biobank. MethodsUK Biobank participants with body fat mass (FM) and fat-free mass (FFM) estimates obtained through BIA (Tanita BC418MA) and DXA concurrently were included. Correlation between BIA and DXA-derived estimates were assessed with Lin’s concordance correlation coefficients. Bland-Altman and Passing-Boblok analyses were performed to quantify the difference and agreement between BIA and DXA. Multivariable linear regression was used to identify predictors influencing the differences. Finally, prediction models were developed to calibrate BIA measures against DXA. ResultsThe analysis included 34437 participants (female 51.4%, mean age 64.1 years at imaging assessment). BIA and DXA measurements were highly correlated (Lin’s concordance correlation coefficient 0.94 for FM and 0.94 for FFM). BIA (Tanita BC418MA) underestimates FM overall by 1.84 kg (23.77 vs. 25.61, p < 0.01), and overestimated FFM overall by 2.56 kg (52.49 vs. 49.93, p < 0.01). The BIA-DXA differences were associated with FM, FFM, BMI and waist circumference. The developed prediction models showed overall good performance in calibrating BIA data. ConclusionOur analysis exhibited strong correlation between BIA (Tanita BC418MA)- and DXA-derived body composition measures at a population level in UK Biobank. However, the BIA-DXA differences were observed at individual level and associated with individual anthropometric measures. Future studies may explore the use of prediction models to enhance the calibration of BIA measures for more accurate assessments in UK Biobank.