Constructing the brief Diagnostic Criteria for Temporomandibular Disorders (bDC/TMD)
Bibliographic record
Abstract
Abstract Background Despite advances in Temporomandibular disorders’ (TMDs) diagnosis, the diagnostic process continues to be problematic in non-specialist settings. Objective To complete a Delphi process to shorten the Diagnostic Criteria for TMD (DC/TMD) to a brief DC/TMD (bDC/TMD) for the diagnoses with the most utility in general dentistry settings. Methods A international Delphi panel was created with 23 clinicians representing major specialities, general dentistry, and related fields. The process comprised a full day workshop, four virtual meetings, six rounds of electronic discussion, and finally an open consultation at a virtual international symposium. Results Within the physical axis (Axis 1) the self-report Symptom Questionnaire of the DC/TMD did not require shortening from 14 items for the bDC/TMD. The compulsory use of the TMD pain screener was removed reducing the total number of Axis 1 items by 18%. The DC/TMD Axis 1 10-section examination protocol (25 movements, up to 12 sets of bilateral palpations) was reduced to 4 sections in the bDC/TMD protocol involving 3 movements and 3 sets of palpations. Axis I then resulted in two groups of diagnoses: painful TMD (inclusive of secondary headache), and common joint-related TMD with functional implications. The Psychosocial Axis (Axis 2) was shortened to an ultra-brief 11 item assessment. Conclusion The bDC/TMD represents a substantially reduced and likely expedited method to establish (grouping) diagnoses in TMDs. This may provide greater utility for settings requiring less granular diagnoses for the implementation of initial treatment, for example non-specialist general dental practice.
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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 | 0.013 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".