Derivation of a minimal clinically important difference score for the WHODAS 2.0 in mild traumatic brain injury
Bibliographic record
Abstract
BACKGROUND: Mild traumatic brain injury (mTBI) treatment research is hindered by lack of clinically meaningful and responsive outcome measures. One promising measure is the World Health Organisation Disability Assessment Schedule 2.0 (WHODAS 2.0), although minimal clinically important differences (MCID) for have not been established. OBJECTIVE: To estimate MCID for the WHODAS 2.0 for mTBI. METHODS: We analysed two prospectively collected mTBI datasets (n = 225) attending adult outpatient clinics in British Columbia, Canada. Participants completed the 12-item WHODAS 2.0, Patient Global Impression of Change scale, and Rivermead Post-Concussion Symptoms Questionnaire. We used anchor- and distribution-based methods to explore MCIDs in WHODAS 2.0 scores. RESULTS: For Study 1 (n = 131), the anchor and distribution-based approaches produced minimal change estimates ranging from 1.3 to 2.8 interval scores. For Study 2 (n = 94), the anchor and distribution-based approaches produced minimal change estimates from 2.2 to 3.2 interval scores. For certain subgroups based on age, sex, and post-concussion severity, minimal change estimates were slightly higher. CONCLUSION: An MCID of 3.5 interval WHODAS 2.0 points would conservatively capture meaningful change in adults of varying age, sex, and post-concussion symptom severity. Such a uniform metric will assist future mTBI intervention studies to improve standards of care and evaluation of outcomes.
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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 | 0.012 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".