Analysis of the sublingual artery using contrast‐enhanced computed tomography
Bibliographic record
Abstract
INTRODUCTION: This in vivo study aimed to clarify the position of the sublingual artery (SLA) relative to the mandibular bone and to infer the potential risk for injury during dental implant surgery. METHODS: Contrast-enhanced computed tomography images of the mouth of 50 edentulous patients (100 sides) treated at Tokushima University Hospital were reviewed. Curved planar reconstructed images perpendicular to the alveolar ridge were processed and classified into molar, premolar, canine, and incisor regions. The SLA and its branches were identified, and the distance from the mandible to the SLA was measured. RESULTS: The SLA was located close to the mandible (<2 mm) in the molar, premolar, canine, and incisor segments in 12.0% (95% confidence interval 5.6%-18.4%), 20.6% (12.6%-28.7%), 30.5% (21.3%-39.8%), and 41.8% (28.8%-54.9%) cases, respectively. The SLA was located within ±3 mm craniocaudally to the upper wall of the mandibular canal in the molar and premolar regions in 50% of cases and within ±5 mm craniocaudally to the mylohyoid ridge in the canine and incisor regions in the other cases, with no sex or age-related differences. The vertical distance from the alveolar ridge to the SLA was influenced by sex and age owing to alveolar resorption, indicating that the alveolar ridge is not a reliable reference for predicting SLA position. CONCLUSIONS: As the risk of SLA injury always exist during dental implant placement and there is no way to confirm the SLA pathways in a patient, clinicians must avoid injuring the sublingual soft tissue.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".