The ShortMAC: Minimum Important Change of a Reduced Version of the Western Ontario and McMaster Universities Osteoarthritis Index
Bibliographic record
Abstract
Study Design Clinical measurement study; secondary analysis of randomized clinical trial data. Background A 12-item shortened version (ShortMAC) of the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC), a condition-specific, patient-reported osteoarthritis index, has been derived, published, and validated. The minimum important change (MIC) of the ShortMAC has not been reported or compared with the traditional 24-item WOMAC. Objectives To investigate the MIC of the 12-item ShortMAC and the traditional 24-item WOMAC across 3 levels of patient-perceived global change. Methods The Management of OsteoArthritis Trial cohort of 206 consecutive patients with knee or hip osteoarthritis was assessed at the initial visit and after 9 weeks of physical therapy (n = 155) or usual medical care (n = 51). The global rating of change instrument, assessed at the 9-week visit, provided the anchor. The MIC was calculated using receiver operating characteristic curve methodology for the ShortMAC and the traditional WOMAC, across 3 levels of patient-perceived change (small, medium, and large change) defined by the global rating of change. Results The MICs for the ShortMAC and traditional WOMAC (both transformed to a scale from 0 to 100) were 7.9 and 9.8 points for small change, 8.4 and 9.8 points for medium change, and 12.1 and 10.1 points for large change, respectively. The MICs of the pain and function subscales are also reported for small, medium, and large changes. Conclusion The lower point estimates for the MIC of the ShortMAC compared with that of the traditional WOMAC, using conventional definitions of MIC and half the number of items, indicate greater efficiency for use in clinical trials and reduced patient burden. J Orthop Sports Phys Ther 2018;48(2):81-86. Epub 21 Oct 2017. doi:10.2519/jospt.2018.7676.
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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 | 0.025 | 0.035 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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; 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".