Should we be concerned about accessory mandibular foramina and canals? A cone-beam computed tomography study
Bibliographic record
Abstract
Objective: Analyze the prevalence of mandibular accessory foramina and canals using cone-beam computed tomography (CBCT). Methodology: 136 mandibles divided into 10 predetermined areas were analyzed through CBCT looking for accessory foramina and canals. The Chi-square and Wilcoxon tests were used. Results: We found 1.316 accessory foramina, which 486 were accompanied by canals. 70.3% of accessory foramina were on the internal mandibular surface, most below the mylohyoid line and genial tubercles. The M1 area had the highest number of foramina, especially in the internal surface. The right mandibular side revealed a significantly greater number of foramina when compared to the left side. The mean diameter of accessory foramina analyzed was 0.85mm. Most of the accessory canals were on the internal mandibular surface, with a longer average length when compared to external surface canals. Conclusion: Our study showed that more detailed studies of accessory mandibular foramina and canals should be carried out, since a high prevalence of these structures and they have not named or classified yet. Furthermore, procedures that reach the internal mandibular surface, especially the anterior region, may be more subject to complications, as well as failure of anesthetic blocks on the right side of the mandible.
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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 | 0.008 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".