Classifications of mandibular canal branching: A review of literature
Bibliographic record
Abstract
AIM: To gather existing radiographic classifications of mandibular canals branching, considering the criteria on which these were based. METHODS: The search for studies on mandibular canals based on imaging exams included literature reviews, epidemiological studies of prevalence, descriptive studies, or case reports. An electronic search in the MEDLINE (OvidSP), PubMed, EMBASE (OvidSP), Web of Science (Thompson Reuters), and Scopus (Elsevier) databases was performed, as well as a manual evaluation of the references of the selected articles. Combinations of key words were placed in each database. No restrictions were imposed regarding the year of publication or language. References collected in duplicate were removed by the authors. A table was drawn up, containing the included studies and respective interest data. RESULTS: Six classifications of mandibular canals branching were selected for the present literature review. Four were based on two-dimensional radiographic exams, and two were performed based on three-dimensional tomographic exams. Three-dimensional classifications were determined based on the analysis found in the least number of exams, comparatively to two-dimensional studies. The prevalence of mandibular canal branching varied from 0% to 38.75% in the works based on two-dimensional exams, while those found in three-dimensional exams ranged from 15.6% to 65%. The studies were mostly referred to branches that began in the mandibular ramus. Just one classification considered the branches that began in the mandibular body region. CONCLUSION: Three-dimensional exams appear to be the best method to view mandibular canal branching. Further studies are warranted to determine its true prevalence and questions concerning to associations.
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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.036 | 0.026 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".