Nocturnal pain and fatigue in middle-aged persons with hip symptoms suspected to be osteoarthritis, is there a link in 10-year follow-up of the CHECK study?
Bibliographic record
Abstract
Objective: To explore the prevalence of nocturnal pain and fatigue in participants with hip symptoms suspected to be early osteoarthritis (OA) and to test the mediating effect of nocturnal pain on the association between hip OA pain and fatigue. Methods: We included participants with hip pain but no knee pain at baseline, from the Cohort Hip and Cohort Knee (CHECK)-study. Severity of hip OA pain was determined using the Numeric-Rating-Scale-pain-score last week. Fatigue was assessed using the SF-36 Fatigue subscale. Nocturnal pain was determined using the WOMAC-question: "How much pain have you experienced in the last 48 h at night while in bed?". Hip OA pain, nocturnal pain and fatigue were measured repeatedly during 10-year follow-up. Path analysis were used per time point to determine the direct effect of OA pain on fatigue and the indirect effect through nocturnal pain. Results: ) the prevalence of nocturnal pain varied between 22 and 35% and the prevalence of fatigue ranged between 14 and 18%. Hip OA pain was associated with nocturnal pain and fatigue. The direct effect of hip OA pain on fatigue was significant at all-time points. No significant mediating effect of nocturnal pain was found. Conclusion: In this cohort of participants suspected to have early hip OA, the prevalence of fatigue remained stable and the prevalence of nocturnal pain decreased slightly over 10-year follow-up. We did not find a mediating effect of nocturnal pain in the pathway between hip OA pain and fatigue.
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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 | 0.002 | 0.006 |
| 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 | 0.000 |
| 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".