Characteristics of intrabony nerve canals in mandibular interforaminal region by using cone‐beam computed tomography and a recommendation of safe zone for implant and bone harvesting
Bibliographic record
Abstract
BACKGROUND: Cone-beam computed tomography can accurately show anatomic structure of intrabony nerve canals in mandibular interforaminal region. PURPOSE: The aim was to evaluate the characteristics of intrabony nerve canals in mandibular interforaminal region by using cone-beam computed tomography (CBCT) and determine a safe zone for implant and bone harvesting. MATERIALS AND METHODS: Hemimandibles (824) CBCT images were obtained. The length of the anterior loop (AL), the length and diameter of the mandibular incisive canal (MIC) and its spatial distance in various landmarks were measured. RESULTS: The prevalence of the AL was 93.57%, and the MIC was 97.33%. The mean lengths of the anterior extension of the anterior loop (aAL), caudal extension of the anterior loop (cAL) and the MIC were 2.53 ± 1.27 mm, 6.04 ± 1.66 mm, 9.97 ± 5.15 mm, respectively. The MIC was closer to buccal border and inferior margin of mandible. The length of the AL and diameter of the MIC varied with gender. CONCLUSIONS: The safe zone recommended for implant surgery is 4 mm anterior and 8 mm inferior to the mental foramen, and 10 mm above the inferior margin of mandible. The chin bone should be harvested at least 10 mm below the tooth apices along with a limited depth of 4 mm.
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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 | 0.002 |
| 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) | 0.001 | 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".