Some remarks on the RDC/TMD Validation Project: report of an IADR/Toronto-2008 Workshop discussion
Bibliographic record
Abstract
A large-scale, multi-site study has been performed to examine the reliability and validity of the research diagnostic criteria for temporomandibular disorders (RDC/TMD) and to suggest revisions of the current RDC/TMD. During an International Association for Dental Research (IADR) Workshop in July 2008, preliminary results of this RDC/TMD Validation Project were presented. One of us was invited to be the critical discussant of the Workshop session in which the Study Group's papers were presented. This article is based on that contribution. One of our concerns relates to the possible circularity and bias, introduced by incorporating the RDC/TMD tests under investigation into the criterion examination. This may have had serious consequences for the outcomes of the validity study as well as for the proposed revisions of the diagnostic algorithms. In addition, a more detailed description of the process of replacing the RDC/TMD tests by other tests is needed. Further, to come to a revised RDC/TMD, it is crucial to know not only how the test outcomes are capable of discriminating between patients with TMD pain and pain-free subjects, as studied in this Validation Project, but also, more importantly, how they discriminate between patients with TMD pain and patients with oro-facial pain (OFP) complaints of non-TMD origin. We welcome the suggestion of an international expert panel to consider, deliberate, and reach consensus on a revised version of the RDC/TMD. Finally, we agree that the suggested expansions of the RDC/TMD taxonomy stress the need for the development of an RDC for OFP, which would include, as an integral part, the revised RDC/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 | 0.130 | 0.163 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.006 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.008 | 0.008 |
| Research integrity | 0.025 | 0.031 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".