Comparing Personalized and non-personalized Outcome Measures in Interdisciplinary Pain Rehabilitation
Bibliographic record
Abstract
Chronic pain, defined as pain persisting for more than three months, affects around 20% of the population and is associated with substantial limitations in daily functioning and quality of life (1,2). Its multidimensional nature requires comprehensive treatment, most commonly delivered through interdisciplinary multimodal pain rehabilitation (IPR). These programs aim to improve functioning and self-management rather than pain reduction alone. Evidence indicates that IPR can reduce pain and disability and may lead to sustained improvements in functioning and participation, although effect sizes are modest (3,4). Evaluation of IPR relies on patient-reported outcome measures (PROMs). Generic PROMs, such as the Pain Disability Index (PDI), assess disability across seven fixed domains (family/home, recreation, social activity, occupation, sexual behavior, self-care, and life-support) on a 0–10 scale (5). Personalized PROMs, such as the Canadian Occupational Performance Measure (COPM), use a semi-structured interview in which patients identify personally meaningful daily activities and rate their performance and satisfaction on 0–10 scales (6). Personalized PROMs may be more sensitive to change than generic measures, because they only capture domains that IPR focussed on(7–10). Although responsiveness of PROMs has been studied in various contexts, direct comparisons between COPM and PDI in IPR are lacking. Previous work has examined correlations between these instruments (12), providing information on their association but not their comparative responsiveness to change. Some PDI domains, such as sexual behavior or work in non-working individuals, may be irrelevant for many patients. Such domains add measurement noise and may reduce responsiveness, suggesting that restricting the PDI to patient-relevant domains could improve its ability to capture change. Moreover, the relationship between changes in these measures and patients’ overall satisfaction with outcomes of IPR has not been established. The present study addresses these gaps by directly comparing the responsiveness of COPM and PDI in IPR, operationalized as pre–post effect sizes. Our primary aim is to compare the magnitude of change detected by COPM and PDI. In addition, we will examine whether restricting the PDI to patient-relevant domains increases its responsiveness, and explore how changes in COPM and PDI relate to overall patient satisfaction with IPR outcomes.
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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 | 0.056 | 0.099 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".