Neighborhood Disadvantage and Knee Osteoarthritis Pain: Do Sleep and Catastrophizing Play a Role?
Bibliographic record
Abstract
OBJECTIVE: The objective was to examine potential pathways linking neighborhood disadvantage to pain severity in individuals with knee pain consistent with or at risk for knee osteoarthritis (KOA). METHODS: The current investigation is a cross-sectional analysis. Data were collected from 140 middle-aged to older non-Hispanic White and non-Hispanic Black adults from the Understanding Pain and Limitations in Osteoarthritic Disease Study 2 (UPLOAD-2). Relationships among neighborhood disadvantage, sleep efficiency, pain catastrophizing, and pain severity were assessed. Neighborhood disadvantage was quantified using the Area Deprivation Index, and actigraphy data were used to assess sleep efficiency. The Coping Strategies Questionnaire-Revised catastrophizing subscale and the Western Ontario and McMaster Universities Osteoarthritis Index pain severity scale were used to assess pain catastrophizing and pain severity, respectively. A serial mediation model assessed the neighborhood-sleep-catastrophizing-pain pathway, as well as the neighborhood-sleep-pain and the neighborhood-catastrophizing-pain pathways. RESULTS: Greater neighborhood disadvantage was associated with worse sleep efficiency, ultimately contributing to greater pain severity. Although neither neighborhood disadvantage nor sleep efficiency were associated with pain catastrophizing, pain catastrophizing itself was associated with greater KOA pain. CONCLUSION: Neighborhood disadvantage impacts KOA pain outcomes through sleep efficiency but not pain catastrophizing, thereby highlighting environmental aspects that impact sleep as potential targets for intervention.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".