Minimal clinically important difference for improvement in six-minute walk test in persons with knee osteoarthritis after total knee arthroplasty
Bibliographic record
Abstract
BACKGROUND: The interpretability of the six-minute walk test (6MWT) in individuals with knee osteoarthritis (OA) is unclear. We aimed to determine the minimal clinically important difference (MCID) for improvement in 6MWT in persons with knee OA at 12 months after total knee arthroplasty (TKA), and if it differed by baseline walking ability. METHODS: Participants with knee OA were assessed 1 month pre- and 12 months post-TKA, including completion of 6MWT. At 12 months, participant-perceived change in walking ability was assessed on an 8-point Likert scale ranging from "extremely worse" to "extremely better". Using logistic regression, ROC curves examined the ability of change in 6MWT distance to discriminate those who perceived walking was improved. MCID was selected overall and then by quartile of baseline 6MWT distance using the Youden method. RESULTS: Two hundred seventy-eight participants were included: mean age 67 years (SD 8.5), 65.5% female, mean pre-TKA 6MWT distance 323.1 (SD 104.7) m, and mean 12-mo 6MWT distance 396.0 (SD 111.9) m. The overall MCID was 74.3 m (AUC 0.65). Acceptable model discrimination (AUC > 0.70) was achieved for individuals in the lowest quartiles of baseline 6MWT distance: Quartile 1: MCID 88.63 m (AUC 0.73); Quartile 2: MCID 84.47 m (AUC 0.72). CONCLUSIONS: In persons with knee OA 12 months post-TKA, 6MWT MCID is dependent on baseline walking ability. Poor model discrimination for those in the highest (best) quartiles of baseline walking ability raise questions about 6MWT use across the full spectrum of walking ability. Further research is needed to better understand use of 6MWT as a performance-based measure of physical function for persons with knee OA.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".