Effectiveness of ultrasonography in the diagnosis of temporomandibular joint disorders: A systematic review and meta‐analysis
Bibliographic record
Abstract
Abstract Background Temporomandibular disorders (TMDs) pose diagnostic challenges, and selecting appropriate imaging modalities is crucial for accurate assessment. This study aimed to compare the diagnostic accuracy and efficacy of ultrasonography (US) and magnetic resonance imaging (MRI) in identifying TMDs. Methods A comprehensive meta‐analysis was conducted, including studies that compared US and MRI for TMJ disorder assessments. Fixed‐effects models were utilized to calculate pooled odds ratios (ORs) and relative risks (RRs) with 95% confidence intervals (CIs). Heterogeneity was assessed using the chi‐squared test and I2 statistic. Newcastle–Ottawa scale was used to assess the methodological quality of the studies included. Results Six studies were included, involving a total of 281 participants. The meta‐analysis demonstrated that MRI was statistically somewhat better than US in identifying TMJ disorders. The summary OR was 0.64 (95% CI: 0.46–0.90), and the summary RR was 0.80 (95% CI: 0.68–0.95). Heterogeneity among the studies was low (χ2 = 2.73, df = 5, p = .74; I2 = 0%). Demographic variables revealed variations in sample size, gender ratio and mean age across the studies. Conclusion This meta‐analysis provides evidence that MRI may be more effective than US in diagnosing TMDs. However, the study is limited by the small number of included studies and variations in demographic variables and study designs. Future research with larger samples and standardised protocols is essential to confirm and strengthen these findings. Understanding the diagnostic accuracy of MRI and US for TMJ disorders will aid clinicians in making informed decisions for effective TMJ disorder assessments and patient management.
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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 | 0.019 | 0.046 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.045 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| 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".