Individualized outcome measures of daily activities are sensitive tools for evaluating hand surgery in rheumatic diseases
Bibliographic record
Abstract
OBJECTIVES: To explore the ability of six outcome measures to capture clinically important changes in patients with rheumatic diseases undergoing hand surgery and to study predictors of changes in activity performance in different patient and surgery strata. METHODS: A total of 172 patients (median age 59 years, disease duration 18 years) were stratified into subgroups: diagnosis, age, general function, type of surgery. Performance of daily activities and satisfaction were assessed by the Canadian Occupational Performance Measure (COPM). Clinically important improvement was defined as a two-step improvement in COPM. Hand function was assessed by reference to grip strength (Grippit), pinch strength (pinch gauge), hand pain (visual analogue scale) and grip ability (Grip Ability Test). Responsiveness was calculated as effect size (ES) at 6-month follow-up compared with baseline. RESULTS: Clinically important improvement was reached by 25-69% depending on outcome measure and type of surgery. Improvement was smaller in patients with multiple simultaneous procedures. Regardless of diagnosis, age, general function and type of surgery, patients improved significantly in all measures, with the largest changes in COPM(performance) and COPM(satisfaction) (ES 0.7-1.9). The ES of pain ranged from 0.2 to 0.7, Grippit from 0.1 to 0.5 and pinch gauge from 0.4 to 0.8. Hand pain was the only significant predictor of clinically important improvement of COPM(performance): odds ratio 0.71, 95% CI 0.51, 0.98 (P = 0.041). CONCLUSION: COPM was the most sensitive instrument to capture clinically important improvement, and hand pain was a significant predictor of improvement, irrespective of diagnosis, age, general functional level and type of surgery.
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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 | 0.005 | 0.014 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".