Association between clinical symptoms and MRI image findings in symptomatic temporomandibular joint (TMJ) disease: A systematic review
Bibliographic record
Abstract
To evaluate the association between clinical signs and symptoms of temporomandibular joint (TMJ) and magnetic resonance image (MRI) findings in patients with temporomandibular disorders (TMD). Relevant articles on humans over 18 years of age were obtained from five databases (Ovid MEDLINE, PubMed, Scopus, Web of Science, and Google Scholar) up to August 2022. Risk of bias assessment was completed using the Joanna Briggs Institute critical appraisal tools. The GRADEpro (Grading of Recommendations, Assessment, Development, and Evaluation) instrument was applied to assess the level of evidence across studies in a GRADE Summary of Findings table. In total, 22 studies were included in this systematic review. Of these, 11 studies highlighted that joint pain was positively associated with particular MRI findings: joint effusion, bone marrow edema, disk displacement with/without reduction, and condylar erosion. Masticatory muscle pain was found to have a strong positive correlation with disk displacement in four studies. Five studies found no significant association between MRI findings and masticatory muscle pain. Range of motion and MRI findings were examined in six studies. Limited mouth opening was found to be correlated with disk displacement in five studies. Of the 11 studies evaluating the correlation between joint noise and MRI findings, eight reported a significant association between disk displacement and TMJ noise. The results suggested that patients with joint pain and limited range of motion may benefit from MRI. Patients exhibiting primarily muscle pain are unlikely to benefit clinically from MRI. Future studies with improved quality are warranted.
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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 | 0.038 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.009 |
| Bibliometrics | 0.010 | 0.011 |
| 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".