Temporomandibular Joint Disorders Following a Motor Vehicle Accident - A Scoping Review for Canadian Dental Professionals.
Bibliographic record
Abstract
OBJECTIVES: The diagnosis, symptom onset, treatment, prognosis, radiographic features and effect of litigation on patients suffering temporomandibular disorders (TMDs) following motor vehicle accidents (MVAs) are still unknown yet highly debated. This review summarizes literature on this topic and provides evidence-based guidance to dental practitioners. METHODS: We applied PRISMA guidelines and their extension for scoping reviews (PRISMA-ScR). The search strategy was defined, and the electronic search included PubMed, MEDLINE, EMBASE, Cochrane Library, Web of Science, and Scopus. Extracted data were organized into categories, and we present a narrative summary of the main findings. RESULTS: We included 37 articles in the review: 15 assessed the diagnosis of TMD following an MVA; 6 assessed onset of symptoms; 13 analyzed treatment options; 10 reviewed prognosis; 6 reviewed imaging findings; and 4 reviewed litigation factors. CONCLUSION: The review revealed heterogeneous results regarding the diagnosis, treatment, prognosis, imaging and litigation factors in MVA-related TMD patients. Future studies are recommended, and no definitive conclusions were drawn.
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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 | 0.008 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.016 | 0.017 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".