The Value of Lateral Cephalometric Variables Measured by Cephalogram in Sex Determining among Iranians
Bibliographic record
Abstract
PURPOSE: Sex determination is one of the most important aspects of the personal identification in forensic medicine. The present study thus aimed to assess the value of cephalogram in determining sex by applying eleven linear and an angular cephalometric variables measured on lateral cephalograms among Iranians.METHODS: In a cross-sectional study, 11 linear and 1 angular cephalometric measurements were studied. Those are: basion to anterior nasal spine, upper facial height, length of cranial base, total face height, frontal sinus height, mastoidale to sella-nasion plan, mastoidale to porion-orbital plan, mastoid height from cranial base, mastoid with at the level of cranial base, mandibular effective length (central condyle to prognation), occipitofrontal diameter, and gonial angle. Measurements were assessed in 150 individuals (75 males and 75 females) aged 25 to 54 years. After preparing lateral cephalograms, the cephalometric measurements were analyzed using PACS software. SPSS version 22.0 was used for analysis. P values of 0.05 or less were considered statistically significant.RESULTS: With the exception of gonial angle, comparison of lateral cephalometric indices between two sexs showed greater values in males than in females (p<0.001). In general, almost all of the cephalometric measurements were found reliable to distinguish between male and female sex skulls with a high sensitivity (100%) and specificity (97.3% to 1000%).CONCLUSION: The cephalometric measurements used in this study are able to differentiate with high specificity and sensitivity between male and female skull
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".