Different Components of Subjective Well-being Are Associated With Chronic Nondisabling and Disabling Knee Pain
Bibliographic record
Abstract
BACKGROUND/OBJECTIVE: Chronic knee pain (CKP) is a common pain complaint in older adults that is often associated with disability. This study investigated the relationship between 2 components of subjective well-being (depressive symptoms and life satisfaction) and CKP phenotypes based on the presence of knee disability. METHODS: A cross-sectional study was performed at baseline of ELSA-Brasil Musculoskeletal cohort (2012-2014). Chronic knee pain phenotypes were identified according to the presence of CKP that was accompanied or not by disability, which was assessed by a question on pain-related limitations to perform everyday activities (overall), Western Ontario and McMaster Universities Osteoarthritis Index's physical function subscale (daily tasks) and 5-times sit-to-stand test (objective). Depressive symptoms were assessed by the Clinical Interview Schedule-Revised and life satisfaction by the Satisfaction With Life Scale. Multinomial logistic regressions used CKP phenotypes as response variables (no CKP as reference). RESULTS: The sample comprised 2898 participants (mean age, 55.9 ± 8.9 years; 52.9% were female). After adjustments for sociodemographic and clinical factors, depressive symptoms were associated with daily tasks disabling CKP (odds ratio [OR], 2.30; 95% confidence interval [CI], 1.45-3.66) and objective disabling CKP (OR, 1.95; 95% CI, 1.29-2.93) and with nondisabling CKP for the overall disability measure (OR, 1.54; 95% CI, 1.17-2.04). Life satisfaction was inversely associated with all phenotypes in fully adjusted models, with strongest magnitude of associations observed for disabling CKP. CONCLUSIONS: The association of depressive symptoms and life satisfaction with CKP phenotypes suggest the need to address both negative and positive components of subjective well-being in the assessment of individuals with knee complaints.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".