The Research Diagnostic Criteria for Temporomandibular Disorders. V: methods used to establish and validate revised Axis I diagnostic algorithms.
Bibliographic record
Abstract
AIMS: To derive reliable and valid revised Research Diagnostic Criteria for Temporomandibular Disorders (RDC/TMD) Axis I diagnostic algorithms for clinical TMD diagnoses. METHODS: The multisite RDC/TMD Validation Project's dataset (614 TMD community and clinic cases, and 91 controls) was used to derive revised algorithms for Axis I TMD diagnoses. Validity of diagnostic algorithms was assessed relative to reference standards, the latter based on consensus diagnoses rendered by two TMD experts using criterion examination data, including temporomandibular joint imaging. Cutoff points for target validity were sensitivity > or = 0.70 and specificity > or = 0.95. Reliability of revised algorithms was assessed in 27 study participants. RESULTS: Revised algorithm sensitivity and specificity exceeded the target levels for myofascial pain (0.82, 0.99, respectively) and myofascial pain with limited opening (0.93, 0.97). Combining diagnoses for any myofascial pain showed sensitivity of 0.91 and specificity of 1.00. For joint pain, target sensitivity and specificity were observed (0.92, 0.96) when arthralgia and osteoarthritis were combined as "any joint pain." Disc displacement without reduction with limited opening demonstrated target sensitivity and specificity (0.80, 0.97). For the other disc displacement diagnoses, osteoarthritis and osteoarthrosis, sensitivity was below target (0.35 to 0.53), and specificity ranged from 0.80 to meeting target. Kappa for revised algorithm diagnostic reliability was > or =0.63. CONCLUSION: Revised RDC/TMD Axis I TMD diagnostic algorithms are recommended for myofascial pain and joint pain as reliable and valid. However, revised clinical criteria alone, without recourse to imaging, are inadequate for valid diagnosis of two of the three disc displacements as well as osteoarthritis and osteoarthrosis.
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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 | 0.018 | 0.058 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".