Cost‐effectiveness of intensive exercise therapy directly following hospital discharge in patients with arthritis: Results of a randomized controlled clinical trial
Bibliographic record
Abstract
OBJECTIVE: To estimate the cost-utility and cost-effectiveness of a 3-week intensive exercise training (IET) program directly following hospital discharge in patients with rheumatic diseases. METHODS: Patients with arthritis who were admitted to the hospital because of a disease activity flare or for elective hip or knee arthroplasty were randomly assigned to either the IET group or usual care (UC) group. Followup lasted 1 year. Quality-adjusted life years (QALYs) were derived from Short Form 6D scores and a visual analog scale (VAS) rating personal health. Function-related outcome was measured using the Health Assessment Questionnaire, the McMaster Toronto Arthritis (MACTAR) Patient Preference Disability Questionnaire, and the Escola Paulista de Medicina Range of Motion scale (EPMROM). Costs were reported from a societal perspective. Differences in costs and incremental cost-effectiveness ratios (ICERs) were estimated. RESULTS: Data from 85 patients (50 IET and 35 UC) could be used for health-economic analysis. VAS personal health-based QALYs were in favor of IET. Function-related outcome showed statistically significant improvements in favor of IET over the first 6 months, according to the MACTAR (P < 0.05) and the EPMROM (P < 0.01). At 1-year followup, IET was euro718 less per patient. The ICER showed a reduction in mean total costs per QALY. In 70% of cases the intervention was cost-saving. CONCLUSION: IET results in better quality of life at lower costs after 1 year. Thus, IET is the dominant strategy compared with UC. This highlights the need for implementation of IET after hospital discharge in patients with arthritis.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| 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 | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".