Prescribing hand strengthening exercise for patients with rheumatoid arthritis; clinical cues influencing occupational therapists' and physiotherapists' judgements
Bibliographic record
Abstract
OBJECTIVE: To explore the clinical judgements of therapists in prescribing the intensity of hand strengthening exercise in rheumatoid arthritis (RA). METHODS: Phase I: Eleven therapists knowledgeable in treating patients with RA subjectively identified seven clinical cues. These were incorporated into 54 hypothetical patient case scenarios. PHASE II: Therapists with ≥2 years post-registration experience and current or recent experience in treating patients with RA were asked to assess 69 case scenarios in total (54 + 15 repeats) and judge what intensity of hand strengthening exercise they would prescribe using the OMNI-Resistance Exercise Scale of perceived exertion. Using responses to the repeated cases, the Cochran-Weiss-Shanteau index of expertise was used to identify therapists who prescribed more consistently. Multiple regression was used to determine which clinical cues were most strongly associated with the intensity of exercise prescribed. A sub-group analysis explored differences between consistent and inconsistent prescribers. RESULTS: Fifty-three therapists took part. Thirty completed all 69 case scenarios. Across all therapists, the three most important clinical cues associated with lower intensity of exercise prescribed were (1) Patient's reported pain intensity whilst practising the exercise (β = -1.150, p < 0.001), (2) Disease activity (β = -0.425, p < 0.001) and (3) average hand pain over the last week (β = -0.353 p < 0.001). Twelve therapists were categorised as consistent prescribers. This group relied on fewer clinical cues (three vs. seven) when judging what intensity of exercise to prescribe. CONCLUSION: This study provides insights into how therapists prescribe hand exercises. Intensity of hand strengthening exercise was influenced by three key clinical cues, including pain intensity and disease activity.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".