Determining Gender and Age by Mandibular Anatomy Landmarks in Computed Tomography with Cone-Beam (CBCT)
Bibliographic record
Abstract
Introduction: this study aimed at determining gender and age by mandibular anatomy landmarks in computed tomography with Cone-Beam (CBCT). Methodology: this cross sectional study was performed on 147 CBCT images available in archive of radiology in the dentistry department of Ahvaz Jondi Shapoor medical science university. In this research, we assessed parameters including SMEF: Distance from mental foramen to the highest point of alveolar crest ridge, BIAC: distance from lowest point of IAC to the most anterior tangent point of buccal mandibular plate, LIAC: distance from the lowest IAC point to the most posterior tangent point o mandibular lingual plate, IMEF: distance from the lowest mental hole border to the lowest tangent point on inferior mandibular border, D2: distance from the lowest IAC canal border to the lowest tangent point on inferior mandibular border and gonial angle: junction of inferior mandibular border and posterior ramus border. Data were analysed by SPSS software 20th version and Spearman correlation coefficient tests, one-way variance analysis, Kruskal-Wallis, independent t, and Uman Withney. Results: SMEF level was significantly different in groups and in 25-34 group it was significantly higher than under 25 group. In right side it was significantly higher than female. IMEF had no significant difference in age groups and in both side it was higher in male than female. BIAC in both sides had no significant difference. LIAC in both sides an in different ages had no significant difference in male and female. D2 had no significant difference in both sides. But in a group with patients older than 55 it was significantly higher than 45-54 group. In addition, in left side it was higher in male than female there was no significant difference in gonial angle in different groups in left side with in right side there was significant difference in different age groups. But there was no significant difference in gender. Conclusion: evaluated indices in this research are not ry accurate to forecast age and gender and they cannot be used as accurate tools in estimating age and gender of people.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".