Minimal important change and difference for knee osteoarthritis outcome measurement tools after non-surgical interventions: a systematic review
Bibliographic record
Abstract
OBJECTIVES: A systematic review. DATA SOURCES: MEDLINE, CINAHL, Web of Science, Scopus and Cochrane databases were searched up to 21 September 2021. ELIGIBILITY CRITERIA: We included studies that calculated MIC and MID using any calculation method including anchor, consensus and distribution methods, for any knee OA outcome tool after non-surgical interventions. DATA EXTRACTION AND SYNTHESIS: We extracted reported MIC, MID and minimum detectable change (MDC) estimates. We used quality assessment tools appropriate to the studies' methods to screen out low-quality studies. Values were combined to produce a median and range, for each method. RESULTS: Forty-eight studies were eligible (anchor-k=12, consensus-k=1 and distribution-k=35). MIC values for 13 outcome tools including Knee injury and Osteoarthritis Outcome Score (KOOS)-pain, activities of daily living (ADL), quality of life (QOL) and Western Ontario and McMaster Universities Arthritis Index (WOMAC)-function were estimated using 5 high-quality anchor studies. MID values for 23 tools including KOOS-pain, ADL, QOL and WOMAC-function, stiffness and total were estimated using 6 high-quality anchor studies. One moderate quality consensus study reported MIC for pain, function and global assessment. MDC values from distribution method estimates for 126 tools including KOOS-QOL and WOMAC-total were estimated using 38 good-to-fair-quality studies. CONCLUSION: Median MIC, MID and MDC estimates were reported for outcome tools in people with knee OA after non-surgical interventions. The results of this review clarify the current understanding of MIC, MID and MDC in the knee OA population. However, some estimates suggest considerable heterogeneity and require careful interpretation. PROSPERO REGISTRATION NUMBER: CRD42020215952.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".