Comparison of the responsiveness of symptomatic outcome measures in knee osteoarthritis
Bibliographic record
Abstract
OBJECTIVE: A number of international scientific societies have recommended a core set of domains to be systematically assessed in clinical research studies on osteoarthritis (OA), i.e., pain, function, and patient's overall assessment. This open, longitudinal, observational study compares the responsiveness of different symptomatic variables evaluating these 3 domains in knee OA. METHODS: Patients were individuals with painful knee OA. The collected data were Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) pain subscale (0-100) and WOMAC function subscale (0-100), Lequesne's index (0-100), pain after physical activities (visual analog scale [VAS] 100 mm), and patient's global assessment (VAS 100 mm). The procedure used was knee joint lovage. Time of collection was before and 1, 3, and 6 months after the lavage. Analysis was by comparison of the standardized response mean (mean of the changes/SD of the changes) in an intent-to-treat strategy after 1, 3, and 6 months using the jackknife method. RESULTS: Improvement in all dimensions of WOMAC subscale scores and VAS scores was observed at month 1. Lequesne's index was not responsive to change. The standardized response mean was moderate, ranging from 0.00 to 0.40. Comparison of the estimates of the standardized response means using the jackknife method showed a statistically significant difference between Lequesne's index and the WOMAC subscale for function, but not between VAS pain and the WOMAC subscale for pain. CONCLUSION: Most of the evaluated variables have a moderate responsiveness. In knee OA, the WOMAC function scale seems to be more sensitive than Lequesne's index for detecting changes after symptomatic therapy.
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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 | 0.026 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".