Anatomical variations of mandibular canal detected by panoramic radiography and CT: a systematic review and meta-analysis
Bibliographic record
Abstract
OBJECTIVES: To investigate the anatomical variations of the mandibular canal through assessment in situ, panoramic radiography, CT or CBCT and assess their frequency. METHODS: Articles were selected from databases (Cochrane Library, LILACS, ProQuest, PubMed, Scopus, Web of Science and Google Scholar), articles without limitations of language, in which the main objective was to evaluate the frequency of bifurcation of the mandibular canal through assessment in situ, panoramic radiography, CT or CBCT were selected. A meta-analysis of prevalence using random effects was performed. RESULTS: Using a selection process in two phases, 15 articles were identified, and a meta-analysis was conducted. The results from these meta-analyses showed that the overall prevalence of anatomical variations for in situ studies was 6.46%, and through assessment of panoramic radiography and CT or CBCT the overall prevalence shown was 4.20% and 16.25%, respectively. CONCLUSIONS: There are two types of variations of the mandibular canal: the retromolar canal and bifid mandibular canal. The frequency variations through assessing in situ, panoramic radiography and CT or CBCT were 6.46%, 4.20% and 16.25%, respectively.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.021 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".