Advanced Ultrasound Screening for Temporomandibular Joint (TMJ) Internal Derangement
Bibliographic record
Abstract
PURPOSE: To present an advanced ultrasound (US) technique and propose its use as a screening diagnostic tool for temporomandibular joint (TMJ) internal derangement. MATERIALS AND METHODS: The technique is based on maintaining the US probe parallel to the articular disc, rather than traditional axial and coronal views, with the position described relative to a clock face. Validation was achieved by direct comparison with magnetic resonance imaging (MRI). A total of 61 patients, with age ranging from 13 to 67 years, were prescreened for TMJ pain and internal derangement, underwent US imaging for screening, and MRI evaluation for final diagnosis. RESULTS: 29 of the 61 patients had disc pathology on MRI. US screening produced no false positive results and only 6 false negative results, corresponding to a sensitivity of 79% and specificity of 100%. Half of the false negative cases involved disc pathology with a medial component to the disc displacement. CONCLUSION: US is both a sensitive and a specific screening tool for TMJ dysfunction when used by an appropriately trained operator, with the exception of medially displaced discs. If TMJ assessment is found to be abnormal, the patient should be referred for MRI, and any patient scheduled for surgery must have the diagnosis confirmed by MRI. If a component of medial disc displacement is suspected, MRI should be performed despite a normal screening US.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".