Recommendations For Core Outcome Domain Set For Whiplash-Associated Disorders (CATWAD)
Bibliographic record
Abstract
OBJECTIVE: Inconsistent reporting of outcomes in clinical trials of treatments for Whiplash-associated Disorders (WAD) hinders effective data pooling and conclusions that can be drawn about the effectiveness of tested treatments. The aim of this study was to provide recommendations for core outcome domains that should be included in clinical trials of WAD. MATERIALS AND METHODS: A 3-step process was used: (1) A list of potential core outcome domains were identified from the published literature. (2) Researchers, health care providers, patients, and insurance personnel participated and rated the importance of each domain via a 3-round Delphi survey. A priori criteria for consensus were established. (3) Experts comprising researchers, health care providers, and a consumer representative participated in a multidisciplinary consensus meeting that made final decisions on the recommended core outcome domains. RESULTS: The literature search identified 63 potential core domains. A total of 223 participants were invited to partake in the Delphi surveys, with 41.7% completing round 1, 45.3% round 2, and 51.4% round 3. Eleven core domains met the criteria for inclusion across the entire sample. After the expert consensus meeting, 6 core domains were recommended: Physical Functioning, Perceived Recovery, Work and Social Functioning, Psychological Functioning, Quality of Life, and Pain. DISCUSSION: A 3-step process was used to recommend core outcome domains for clinical trials in WAD. Six core domains were recommended: Physical Functioning, Perceived Recovery, Work and Social Functioning, Psychological Functioning, Quality of Life, and Pain. The next step is to determine the outcome measurement instruments for each of these domains.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.387 | 0.512 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.006 | 0.021 |
| Bibliometrics | 0.014 | 0.008 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.009 | 0.012 |
| Research integrity | 0.014 | 0.013 |
| Insufficient payload (model declined to judge) | 0.008 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".