The use of bone scintigraphy in temporomandibular joint disorders
Bibliographic record
Abstract
INTRODUCTION: The use of bone scintigraphy (bone scan) in the diagnosis of temporomandibular joint (TMJ) disease has been infrequent, as compared with traditional radiographic techniques. Bone scans have the potential to detect active bone remodeling whereas corresponding radiographs may be normal or document past structural change in the joint. Traditional radiographic findings and relevant clinical signs and symptoms correlated with bone scans may aid in the diagnosis of TMJ disease and possibly affect treatment and prognosis of individual cases. The use of bone scans as an additional tool in diagnosing TMJ disease was assessed in this series of patients. METHODS: Thirty consecutive subjects with TMJ tenderness were selected for bone scintigraphy using technetium diphosphonate 99 mTc and single photon emission computerized tomography. These subjects received bone scans as well as other selected imaging modalities for diagnostic purposes. RESULTS AND DISCUSSION: The findings on bone scan were evaluated and a change in preliminary clinical diagnosis or treatment was made in 60% of cases because of the findings on bone scintigraphy. Bone scintigraphy may be valuable to assess progress of TMJ inflammation or remodeling, and may affect diagnosis and treatment of patients with TMJ tenderness.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".