Clinimetric Properties of Self-reported Disability Scales for Whiplash
Bibliographic record
Abstract
OBJECTIVES: A core outcomes set (COS) for whiplash-associated disorders (WADs) has been proposed to improve consistency of outcome reporting in clinical trials. Patient-reported disability was one outcome of interest within this COS. The aim of this review was to identify the most suitable tools for measuring self-reported disability in WAD based on clinimetric performance. METHODS: Database searches took place in 2 stages. The first identified outcome measures used to assess self-reported disability in WAD, and the second identified studies assessing the clinimetric properties of these outcome measures in WAD. Data on the study, population and outcome measure characteristics were extracted, along with clinimetric data. Quality and clinimetric performance were assessed in accordance with the Consensus-based Standards for the Selection of Health Status Measurement Instruments (COSMIN). RESULTS: Of 19,663 records identified in stage 1 searches, 32 were retained following stage 2 searches and screening. Both the Whiplash Disability Questionnaire and Neck Disability Index performed well in reliability (intraclass correlation coefficient=0.84 to 0.98), construct validity (74% to 82% of hypotheses accepted), and responsiveness (majority of correlations in accordance with hypotheses). Both received Category B recommendations due to a lack of evidence for content validity. DISCUSSION: This review identified the Neck Disability Index and Whiplash Disability Questionnaire as the most appropriate patient-reported outcome measures (PROMs) for assessing self-reported disability in WAD based on moderate to high-quality evidence for sufficient reliability, construct validity and responsiveness. However, the content validity of these PROMs has yet to be established in WAD, and until this is undertaken, it is not possible to recommend 1 PROM over the other for inclusion in the WAD COS.
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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 | 0.096 | 0.251 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.008 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".