A core outcome set for clinical trials in whiplash-associated disorders (WAD): a study protocol
Bibliographic record
Abstract
BACKGROUND: Whiplash-associated disorders (WAD) as a consequence of a motor vehicle crash are a costly health burden in Western societies. Up to 50% of injured people do not fully recover. There have been numerous clinical trials and cohort studies conducted for WAD with many varied outcome measures used, making data pooling difficult and hindering meta-analysis. These issues could be addressed through the development of a core outcome set (COS) that should be included in all clinical trials for WAD. The purpose of this project is to develop and disseminate a COS for clinical trials in WAD. METHODS/DESIGN: An international Steering Committee was formed to initiate and support the development of this COS. The project will comprise five phases: (1) a comprehensive review of core outcome domains used in clinical trials in WAD, (2) an international Delphi survey including individuals with WAD, health care providers, clinical researchers and insurance personnel to define the core outcome domains, (3) a meeting of relevant stakeholders to reach consensus regarding the final core outcome domains, (4) identification and evaluation of instruments used to measure the final core outcome domains, and (5) a consensus meeting to agree on the core outcome measurement instruments to be used. DISCUSSION: The aim of this proposal is to complete a five-stage process to develop a COS for all clinical trials in WAD. An implementation strategy will also be proposed.
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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 | 0.288 | 0.238 |
| Meta-epidemiology (narrow) | 0.005 | 0.004 |
| Meta-epidemiology (broad) | 0.007 | 0.009 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.012 | 0.010 |
| Insufficient payload (model declined to judge) | 0.031 | 0.009 |
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".