Minimal Clinically Important Difference as Applied in Rheumatology: An OMERACT Rasch Working Group Systematic Review and Critique
Bibliographic record
Abstract
OBJECTIVE: We aimed to evaluate how minimal (clinically) important differences (MCID/MID) were calculated in rheumatology in the past 2 decades and demonstrate how the calculation is compromised by the lack of interval scaling. METHODS: We conducted a systematic literature review on articles reporting MCID calculation in osteoarthritis (OA) and rheumatoid arthritis (RA) from January 1, 1989, to May 9, 2014. We evaluated the methods of MCID calculation and recorded the ranges of MCID for common patient-reported outcome measures (PROM). Taking data from the Health Assessment Questionnaire (HAQ), we showed the effects of performing mathematical calculations on ordinal data. RESULTS: A total of 330 abstracts were reviewed and 123 articles chosen for full text review. Thirty-six (19 OA, 16 RA and 1 OA-RA) articles were included in the final evaluation. The Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) was the most frequently reported PROM with relevant calculations in OA, and the HAQ in RA. Sixteen articles used anchor-based methods alone for calculation of MCID, and 1 article used distribution-based methods alone. Nineteen articles used both anchor and distribution-based methods. Only 1 article calculated MCID using an interval scale. Wide ranges in MCID for the WOMAC in OA and HAQ in RA were noted. Ordinal-based derivations of MCID are shown to understate true change at the margins, and overstate change in the mid-range of a scale. CONCLUSION: The anchor-based method is commonly used in the calculation of MCID. However, the lack of interval scaling is shown to compromise validity of MCID calculation.
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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 | 0.405 | 0.642 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.013 | 0.021 |
| Bibliometrics | 0.025 | 0.019 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.007 | 0.006 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".