Sex estimation from first rib 3D morphometry in a Thai population: Establishing baseline standards through comparative evaluation of traditional statistical and machine learning approaches

< 0.01 with Cohens d ranging from 0.57 to 1.38. Length from tubercle to rib head with d = 1.38, distance from tubercle to internal margin with d = 1.17, and weight with d = 0.94 showed strongest discrimination. Comparative analysis revealed overall accuracy ranging from 75.2% to 81.0%. Random Forest achieved highest performance with 81.0% accuracy and AUC of 0.867 alongside 4.6% sex disparity. BLR demonstrated 80.3% accuracy with perfect balance showing 0% disparity, while DFA yielded 80.2% with 2.3% disparity. SVM underperformed at 75.2% with substantial bias of 19.0% disparity. This study provides the first validated sex estimation standards for Thai first rib morphometry. Binary logistic regression emerges as optimal for forensic application, balancing 80.3% accuracy, perfect sex-specific performance, and methodological transparency essential for legal proceedings, despite Random Forests marginally superior discriminatory capacity.