Diagnostic criteria for headache attributed to temporomandibular disorders
Bibliographic record
Abstract
AIMS: We assessed and compared the diagnostic accuracy of two sets of diagnostic criteria for headache secondary to temporomandibular disorders (TMD). METHODS: In 373 headache subjects with TMD, a TMD headache reference standard was defined as: self-reported temple headache, consensus diagnosis of painful TMD and replication of the temple headache using TMD-based provocation tests. Revised diagnostic criteria for Headache attributed to TMD were selected using the RPART (recursive partitioning and regression trees) procedure, and refined in half of the data set. Using the remaining half of the data, the diagnostic accuracy of the revised criteria was compared to that of the International Headache Society's International Classification of Headache Diseases (ICHD)-II criteria A to C for Headache or facial pain attributed to temporomandibular joint (TMJ) disorder. RESULTS: Relative to the TMD headache reference standard, ICHD-II criteria showed sensitivity of 84% and specificity of 33%. The revised criteria for Headache attributed to TMD had sensitivity of 89% with improved specificity of 87% (p < 0.001). These criteria are (1) temple area headache that is changed with jaw movement, function or parafunction and (2) provocation of that headache by temporalis muscle palpation or jaw movement. CONCLUSION: Having significantly better specificity than the ICHD-II criteria A to C, the revised criteria are recommended to diagnose headache secondary to TMD.
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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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".