Temporomandibular joint dysfunction in some rheumatological diseases (case–control study)
Bibliographic record
Abstract
Background The incidence of temporomandibular joint (TMJ) involvement in inflammatory arthritis, such as rheumatoid arthritis, is often underestimated. Noninvasive imaging modalities such as musculoskeletal ultrasound (MSUS) are used to evaluate the TMJ. Aim This study was conducted to determine the affection of TMJ in some rheumatological diseases using MSUS. Patients and methods The study included 50 participants divided into two groups: group I included 40 patients (80 TMJ joints) with four rheumatological diseases, and group II included 10 apparently healthy persons (20 TMJ joints) matched in age and sex with the patients, chosen as a control group. All patients were diagnosed clinically and through laboratory investigations. Thereafter, they were assessed for pain degree by visual analog scale (VAS); disease activity using disease activity score-28, systemic lupus erythematosus disease activity index, and Western Ontario and McMaster Universities Arthritis Index; functionally using Modified Health Assessment Questionnaire, and Fonseca questionnaire; and underwent imaging assessment using panoramic radiography and musculoskeletal ultrasonography. Results Erosions were detected in 12.5% of the examined TMJs by panoramic radiography, whereas they were detected in 32.5% by MSUS. TMJ effusion and disc displacement could not be detected by panoramic radiography, whereas effusion was detected by MSUS in 23.8% of the examined TMJs and disc displacement was detected in 27.5%. Conclusion MSUS is more sensitive in detection of TMJ affection than panorama x-ray. Radiographic pathological findings by MSUS and panorama x-rays were more in temporomandibular joint disorders of patients with rheumatoid arthritis followed by systemic lupus erythematosus and then osteoarthritis, and finally, psoriatic arthritis.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".