Evaluation of the presence and generalcharacterization of canalis sinuosus basedon cone-beam computed tomography images:a literature review
Bibliographic record
Abstract
Canalis sinuosus (CS) is a small branch of infra-orbital canal that contains anatomical structures of clinical significance in dentist's daily practice.Accurate identification of CS and its branches is crucial for reducing complications in dental treatments.Therefore, knowledge about CS is systematically expanding, and its importance is increasing.The aim of this study was to conduct a literature review on the prevalence, location, diameter, and trajectory variations of CS as well as the influence of age, gender, and surgical implications.A systematic review of the literature was performed.Two reviewers gathered and analyzed articles from electronic databases, including PubMed, Semantic Scholar, and Google Scholar.The frequency of CS presence was evaluated through a systematic review conducted using MedCalc, with a significance level of 5%.Due to significant heterogeneity among included papers, a random effects model was applied.Out of 276 initially identified articles, 12 studies were eventually included in the review.All were based on cone-beam computed tomography (CBCT) examinations and their results.The systematic review showed that overall prevalence of CS was 54.44%, CS presence among women was 56.76% and men 43.24%, unilateral CS was observed in 41.78%, whereas bilateral was found in 58.22% of CBCT images.CS may exhibit variability in trajectory, location, and diameter.The analyzed articles did not demonstrate statistically significant differences in age.Recent research indicates an increasing prevalence of CS, possibly due to greater observer awareness regarding its presence.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".