The Difference of Mandibular Dimensions Using Cone Beam Computed Tomography for Sex Determination in Forensic Odontology: A Systematic Literature Review
Bibliographic record
Abstract
Mandibular assessment using cone beam computed tomography (CBCT) can accurately provide the measurements for reconstructing and imaging of craniofacial structures in sex determination. The aim of this study was to determine the difference of mandibular dimensions between male and female and the greater mandibular measurement for sex determination using CBCT. Searching literatures was assisted by, into consideration four journal databases including PubMed, EBSCO, Pro Quest, and Google Scholar until March 3, 2021 with PRISMA method and relevant keywords. Review of literature quality was done by Newcastle-Ottawa Quality Assessment Scale (NOS). Inclusion criteria were population (male and female mandibles), intervention (CBCT), and outcome (differences of mandibular measures in males and females). Eight literatures were used in the study to analyze the difference of mandibular dimension with total sample of 1252 mandibles. Mean of mandibular dimensions through bigonial breadth, bicondylar breadth, ramus length, mandibular length, minimum ramus breadth, and gonial to gnathion length were significantly higher in males than females. Mean of gonial angle measure was significantly higher in female than male according to Tassoker et al, Abofakher et al, and El-Fotouh et al. Conversely, Gamba et al. showed the mean value of gonial angle was significantly higher in males than females. The most accurate mandibular measurement sorted from seven literature studies were bigonial breadth, ramus length, gonial angle, bicondylar breadth, mandibular length, minimum ramus breadth, and gonial to gnathion length. It suggests that according to seven varied studies the most accurate mandibular dimensions showed in different values as a result ofdiverse population of each study.
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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 | 0.006 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.015 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".