The Longitudinal Examination of Arthritis Pain (LEAP) study: relationships between weekly fluctuations in patient-rated joint pain and other health outcomes.
Bibliographic record
Abstract
OBJECTIVE: To examine relationships between weekly fluctuations in self-rated joint pain and other health outcomes among adults with osteoarthritis (OA). METHODS: In this observational study, 287 adults (aged > or = 50 yrs) with hip or knee OA were recruited from 16 medical practices across the United States. Patients were telephoned weekly for 12 weeks to assess pain/stiffness, daily activities/function, productivity, emotional well-being, quality of life, and healthcare utilization. Associations between changes in joint pain levels and other health outcomes were evaluated using a generalized estimating equation model. RESULTS: The mean (SD) pain score at Week 1 was 4.2 (2.1) on the Western Ontario and McMaster Universities OA index (WOMAC) pain subscale (0 = no pain, 10 = extreme pain); during the study, 49% of patients reported a between-week fluctuation of > or = 2 points. A 2-point decrease in WOMAC pain subscale score was associated with a 22% decrease in number of days of limited activity/week (beta = -0.107; 95% confidence interval -0.163, -0.051); a 48% decrease in number of days of missed work/week (beta = -0.217; 95% CI -0.395, -0.039); a 14% decrease in number of nights with pain-related sleep interference/week (beta = -0.068; 95% CI -0.109, -0.027). Patients were 1.6 times more likely to contact a healthcare provider when their pain changed from "acceptable" to "unacceptable." CONCLUSION: Weekly fluctuations in pain levels and other health outcomes were identified among adults with OA. Decreases in patient-reported pain were associated with improvements in daily activities/functioning and decreases in work absenteeism, sleep interference, and healthcare resource use.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".