Introduction Acute aluminum phosphide (AlP) poisoning is associated with high mortality. Early identification of high-risk patients remains challenging. Machine learning may enhance risk stratification by integrating complex clinical and laboratory patterns. Methods Two-year retrospective study included 252 adult symptomatic patients with acute AlP poisoning to develop and evaluate machine learning ensemble models for predicting mortality. Demographic, clinical, electrocardiographic, and laboratory data obtained at admission were extracted from medical records. Four ensemble machine learning models were developed: Gradient Boosting, HistGradient Boosting, Random Forest, and Voting classifier. Data preprocessing, feature selection, correlation, feature importance analysis, and K-fold stratified cross-validation were performed. Models’ performance was evaluated using accuracy, precision, recall, F1-score and Receiver Operator Characteristic curve analysis. The final model was calibrated, and decision curve analysis was performed. Results Mortality rate was 65.9%. Gradient Boosting demonstrated best overall predictive performance (accuracy 0.84, F1-score 0.88, AUC 0.86). The four models demonstrated comparable areas under the curve. Across models, mean arterial pressure, serum bicarbonate, oxygen saturation, and Glasgow Coma Scale were consistently the most influential predictors. Cross-validation of Gradient Boosting model showed performance plateauing after top 4 features. Evaluating final Gradient Boosting model based on top 4 features revealed accuracy was 0.86, precision 0.93, recall 0.85, F1-score 0.89, and AUC 0.94. Conclusion Our findings highlight the potential of utilizing interpretable machine learning ensemble models in risk stratification and decision-making in acute AlP poisoning. Gradient Boosting offers an efficient tool linking hemodynamic instability, metabolic acidosis, impaired oxygenation, and neurological status to mortality.
Introduction Clinical Toxicology is a medical subspecialty dedicated to the management of acute poisoning. Medical toxicologists investigate how pharmaceuticals, chemicals, plant poisons, and venoms affect humans. Aim This study aimed to explore the experiences of Egyptian female clinical toxicologists, including current practices, initiatives, motivations to pursue clinical toxicology. Furthermore, we aimed at identifying challenges and threats related to female clinical toxicologists’ professional life. Methods A cross-sectional, questionnaire-based study was conducted among 93 Egyptian female clinical toxicologists with a mean age of 37.7±7.2 years. Results Forensic Medicine and Clinical Toxicology Department was the primary practice setting. Current toxicology practices focused on academic roles (67.7%) and poison control centre activities, including in-person consultation (52.7%) and call-centre services (30.1%), as well as research (34.4%) and toxi-lab work (23.7%). Interest in clinical toxicology emerged during residency (39.8%) and college (30.1%). The decision to pursue clinical toxicology was driven primarily by job selection (51.6%). The most significant motivators were a positive impact on public health (47.3%) and mentorship. The main obstacles facing women were low earnings (72.0%) and inadequate infrastructure (54.8%). Work was negatively affected by violence in 47.3% of respondents. Years of experience were positively correlated with motivation (rho=0.227, P=0.029), perception (rho=0.215, P=0.038), and attitude (rho=0.262, P=0.011). Conclusions Policies should prioritize strengthening clinical toxicology capacity by upgrading infrastructure and laboratory/equipment resources, improving financial remuneration and career sustainability, and implementing robust workplace safety measures to prevent violence and reduce burnout risk.
