The Performance and Association Between Patient-reported and Performance-based Measures of Physical Functioning in Research on Individuals with Arthritis
Bibliographic record
Abstract
OBJECTIVE: To evaluate the association between patient-reported outcome (PRO) and performance-based (PB) measures of physical functioning (PF) among individuals with self-identified arthritis to inform decisions of which to use when evaluating the effectiveness of a physical activity intervention. METHODS: Secondary data analysis of a nonrandomized 2-arm pre-post community trial of 462 individuals who self-identified as having arthritis and participated in the Walk with Ease (WWE) intervention. Two PRO and 8 PB assessments were collected at baseline (preintervention) and at 6-week followup. We calculated correlations between PB and PRO measures, assessed how measures identified changes in PF from baseline to followup, and compared PRO and PB measures to arthritis symptoms of pain, stiffness, and fatigue. RESULTS: Strength of correlations between PB and PRO measures varied depending on the PB measure, ranging from 0.21 to 0.54. PRO and PB measures identified PF improvements from baseline to followup, but none showed significant differences between the 2 WWE modalities (instructor-led or self-directed groups). Correlations with arthritis symptoms were stronger for PRO (0.30-0.46) than PB measures (0.03-0.31). CONCLUSION: PRO measures may provide us with insights into aspects of PF that are not identified by PB measures alone. Use of PRO measures allows patients to communicate their perceptions of PF, which may provide a more accurate representation of overall PF. Our study does not suggest abandoning the use of PB measures to characterize PF in patients with self-identified arthritis, but recommends that PRO measures may serve as complementary or surrogate endpoints for some studies.
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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 | 0.031 | 0.067 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".