Is Handheld Ultrasonography Reliable as a Primary Imaging Modality for the Diagnosis of Internal Derangement and Degenerative Joint Disease of the Temporomandibular Joint?
Bibliographic record
Abstract
BACKGROUND: Magnetic resonance imaging (MRI) is the gold standard imaging modality for diagnosing internal derangement (ID) and degenerative joint disease (DJD) of the temporomandibular joint (TMJ). MRI disadvantages include cost, availability, and patient discomfort, making it worthwhile to determine if handheld ultrasonography (HHU) may reduce MRI utilization. PURPOSE: The purpose of this study was to measure and compare the diagnostic accuracy of HHU to the MRI gold standard for the diagnosis of ID and DJD. STUDY DESIGN: This retrospective, cohort, single-institutional study included participants with suspected ID and DJD referred by dentists and primary care physicians to the Oral and Maxillofacial Surgery Clinic at the Health Sciences Centre in Winnipeg, Manitoba. PREDICTOR VARIABLE: The predictor variable was the HHU diagnosis of the TMJ for the presence or absence of anterior disc displacement (ADD) and DJD. OUTCOME VARIABLE: The outcome variable was the MRI diagnosis of the TMJ for the presence or absence of ADD and DJD. COVARIATES: Covariates include demographics, medical history, and assessments of ID and DJD based on clinical examination. ANALYSES: The diagnostic accuracy of HHU relative to MRI was determined using appropriate statistical tests with a significance level (P value) of ≤0.05 and a 95% CI. RESULTS: The sample consisted of 20 subjects (mean age: 47 years, SD 13; 17 female, 85%), who were scanned using HHU and MRI. MRI was the comparative standard, and when detecting ADD without reduction, HHU demonstrated specificity of 100.00%, positive predictive value (PPV) of 100.00%, and a kappa value of .93. For ADD with reduction, HHU demonstrated specificity of 100.00%, PPV of 100.00%, and a kappa value of .79. For DJD, HHU demonstrated specificity of 89.5%, PPV of 20%, and a kappa value of .23. CONCLUSION: HHU demonstrated statistically significant specificity and PPV when assessing ID and shows promise as a screening tool for MRI referrals, helping to identify patients who can be maintained on conservative treatment. The diagnostic ability of HHU for the detection of DJD was not statistically significant.
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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 | 0.005 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".