Gender Dimorphism of Crania in Young Jordanian Adults: Discriminant Function Analysis Approach for Gender Prediction
Bibliographic record
Abstract
Background: Determination of gender using craniofacial characteristics, though made common worldwide, Jordan is still lacking of such investigation. The aim of this study was to develop a gender determination technique for young Jordanian adult population using osteometric data, from c ephalometric images, and discrimina nt function analysis. Methods: A total of 146 randomly selected digital lateral cephalometric radiographs of fully dentate young adult patients were used in the investigation; 47 patients were males and 99 were females. For each lateral cephalometric radiograph 19 craniofacial skeletal landmarks were digitized by one observer using a customized analysis created in Viewbox 4-Cephalometric Software subroutines. Utilizing the digitized landmarks, 18 measurements that comprised 14 linear, three angular and one proportional parameters were carried out. Results: The results demonstrated that, with the exception of the Menton to Gonion distance, i.e. the length of the mandibular body, the mean values of all other parameters of male subjects were statistically significantly larger (P < 0.05) than those for females. Mastoid height was found to be the best single predictor of gender and can provide an accuracy rate of 82.2%. The stepwise method revealed four dimensions (mastoid height, mastoid width, glabella to supraglabellare-nasion distance, and the length of skull base) were found to form the best combination of parameters most precisely to depict the best possible prediction, raising the classification accuracy up to 87.7%. Conclusions: A discriminant function equation specific for Jordanian population has been derived from cranio-mandibular variables. The equation can now be used for a calculable and more precise prediction of gender of Jordanian young adult population. J Curr Surg. 2014;4(3):76-85 doi: http://dx.doi.org/10.14740/jcs241w
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.002 | 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".