Forensic sex classification using cut-off values from cranio-mandibular radiologic parameters
Bibliographic record
Abstract
Introduction:In the context of forensic identification, sex determination is the first important step, and the mandible is of particular interest because it shows clear signs of dimorphism.Objectives: The purpose of this investigation was sex classification through the application of several craniomandibular metric variables in a Portuguese adult population.Material and methods: 206 orthopantomographic images (116 females and 90 males) were selected from Portuguese adults aged between 25 and 79 years.From each image, 14 linear variables were measured using ImageJ® software.IBM SPSS® software version 27.0 was applied to analyze data presented as extremes, mean, standard deviation, and median.Statistical methodologies included intra-class analysis, normality test, t-test, Mann-Whitney test, logistic regression, and ROC curve to determine cut-off point and respective sensitivity and specificity of sex classification.Results: Comparing the two sexes, all variables showed significant differences, except for the angular variables.Variables with the best area under ROC curve were height of the right (0.861) and left (0.850) mandibular ramus.Based on the logistic regression, the left coronoid height, right mandible body height, left mandible body height, and right mandible ramus height showed an accuracy level of 83.0%, sensitivity of 86.2%, and specificity of 78.9% in sex classification. Conclusions:The obtained results show identifiable mandibular dimorphism in the measurements of mandibular structures in orthopantomography images.These metric variables allow for the creation of discrimination models between males and females in a Portuguese adult population.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.009 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".