Multidimensional minimal clinically important differences in knee osteoarthritis after comprehensive rehabilitation: a prospective evaluation from the Bad Zurzach Osteoarthritis Study
Bibliographic record
Abstract
OBJECTIVE: To determine minimal clinically important differences (MCIDs) for improvement and worsening in various health dimensions in knee osteoarthritis under conservative therapy. METHODS: Health, symptoms and function were assessed by the generic Short Form 36 and the condition-specific Western Ontario and McMaster Universities Osteoarthritis Index in n=190 patients with knee osteoarthritis before and after comprehensive rehabilitation intervention (3-month follow-up). By means of construct-specific transition questions, MCIDs were defined as the difference between the 'slightly better/worse' and the 'almost equal' transition response categories according to the 'mean change method'. The bivariate MCIDs were adjusted for sex, age and baseline score to obtain adjusted MCIDs by multivariate linear regression. They were further standardised as (baseline) effect sizes (ESs), standardised response means (SRMs) and standardised mean differences (SMDs) and compared with the minimal detectable change with 95% confidence (MDC95). RESULTS: Multivariate, adjusted MCIDs for improvement ranged from 2.89 to 16.24 score points (scale 0-100), corresponding to ES=0.14 to 0.63, SRM=0.17 to 0.61 and SMD=0.18 to 0.72. The matching results for worsening were -5.80 to -12.68 score points, ES=-0.30 to -0.56, SRM=-0.35 to -0.52 and SMD=-0.35 to -0.58. Almost all MCIDs were larger than the corresponding MDC95s. CONCLUSIONS: This study presents MCIDs quantified according to different methods over a comprehensive range of health dimensions. In most health dimensions, multivariate adjustment led to higher symmetry between the MCID levels of improvement and worsening. MCIDs expressed as standardised effect sizes (ES, SRM, SMD) and adjusted by potential confounders facilitate generalisation to the results of other studies.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| 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".