Outcomes of patients with rheumatoid arthritis receiving rehabilitation
Bibliographic record
Abstract
PURPOSE OF REVIEW: Rehabilitation, including physical therapy and occupational therapy, complements drug therapy in the management of symptoms in patients with rheumatoid arthritis. Approximately 26% of patients with rheumatoid arthritis receive a referral for rehabilitation by rheumatologists. This review summarizes findings on the effectiveness and economic outcomes of physical therapy and occupational therapy in managing rheumatoid arthritis. RECENT FINDINGS: Studies evaluating the outcomes of various service delivery models for physical therapy and occupational therapy demonstrate improvements, especially in physical function, among people with rheumatoid arthritis. A recent pilot study examining the primary therapist model also suggests that the primary therapist model may be a viable option for delivering rheumatoid arthritis rehabilitation services. However, the evidence on other alternative models such as the physical therapy/occupational therapy practitioner model is limited. Only a few economic evaluations have been performed, and among those, none examine the cost-effectiveness of different service models. SUMMARY: Systematically interpreting the findings of service delivery models in rehabilitation is challenging because of the wide range of interventions and outcome measures used. A thorough understanding of the value of different rehabilitation models will require the guidance of a sound evaluation framework. Future clinical trials should consider including a component for evaluating cost-effectiveness. Such knowledge can contribute to evidence-informed resource allocation.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".