Effectiveness of the introduction of an International Classification of Functioning, Disability and Health–based rehabilitation tool in multidisciplinary team care in patients with rheumatoid arthritis
Bibliographic record
Abstract
OBJECTIVE: To investigate whether the use of an International Classification of Functioning, Disability and Health (ICF)-based instrument to structure multidisciplinary care improves clinical effectiveness and satisfaction in patients with rheumatoid arthritis (RA) admitted for multidisciplinary team care. METHODS: Consecutive patients with RA admitted to an inpatient or day patient multidisciplinary team care ward were included during a 12-month period before (period I) and after (period II) the introduction of an ICF-based rehabilitation tool (Rehabilitation Activities Profile [RAP]). Patients were assessed at admission, discharge, and 6 weeks thereafter. The primary outcome measure was a patient-oriented measure of functional ability (McMaster Toronto Arthritis Patient Preference Disability Questionnaire [MACTAR]), whereas secondary outcome measures included measures of physical and mental functioning, quality of life, disease activity, and patient satisfaction. Change scores between periods were compared using analysis of covariance. RESULTS: A total of 80 and 85 patients were included in periods I and II, respectively. Concerning the improvement of the MACTAR score and all other secondary clinical outcome measures, there was no significant difference between the 2 periods. Patient satisfaction with care was slightly higher in period II than in period I, with the differences regarding the total score of a multidimensional satisfaction questionnaire and the domains focusing on individual problems and empathy reaching statistical significance. CONCLUSION: The introduction of the RAP did not change clinical effectiveness but had a modest beneficial impact on patient satisfaction with care in patients with RA admitted for multidisciplinary team care.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".