Evaluation of lingual mandibular depression of submandibular salivary glands and its relation to inferior alveolar canal using cone beam computed tomography in Malaysian population: a cross sectional study
Bibliographic record
Abstract
Introduction:In the forensic field, different races or ethnicity present various skeletal status or skull pattern, which aid in racial identification.Objectives: Our study evaluated the deepest lingual concavity depth in the posterior region of the mandible, and its relation to the inferior alveolar canal using cone beam computed tomography (CBCT), and to associate the findings to different gender, age, and race in Malaysia.Material and methods: A retrospective analysis of 384 CBCT scans was done on both left and right sides of the mandible.Position of the deepest lingual concavity depth was evaluated in relation to the inferior alveolar canal, and categorized into 3 groups: above the canal, at the same level as the canal, and below the canal.Analysis was carried out for different gender, age groups, and races in Malaysia. Results:The mean age of the study participants was 36.6 ± 14.6 years, and there was no significant relationship between the deepest lingual concavity depth and age.The mean lingual concavity depth was 2.23 ± 0.76 mm in male subjects, and 1.74 ± 0.61 mm in female subjects.The mean lingual concavity depth was highest in Indian (2.20 ± 0.73 mm), followed by Malay (2.00 ± 0.73 mm), and Chinese (1.80 ± 0.69 mm) populations.Conclusions: Due to variations in the depth and the risk of penetration, with decision made solely on lingual concavity depth and its relation to the inferior alveolar canal, the assessment of implant fixture site is recommended to be done on a case-by-case basis during pre-operative planning for dental implant surgery.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".