Impact of self-reported comorbidity on physical and mental health status in early symptomatic osteoarthritis: the CHECK (Cohort Hip and Cohort Knee) study
Bibliographic record
Abstract
OBJECTIVE: To describe the relationship between comorbidity (absolute number as well as the presence of specific comorbidities) and pain, physical functioning and mental health status of participants with early symptomatic OA of the hip or knee. METHODS: In the Netherlands, a prospective 10-year follow-up study was initiated by the Dutch Arthritis Association in participants with early symptomatic OA of the hip or knee: CHECK (Cohort Hip and Cohort Knee), which consists of 1002 individuals. At baseline, linear regression analysis was used to determine the influence of comorbidity on the outcome variables: Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) pain, WOMAC physical functioning, Medical Outcomes Study Short Form 36 (SF-36) Physical Component Summary and Mental Component Summary. RESULTS: Of 979 subjects, 67% reported one or more comorbidities. After controlling for age, gender, social status and severity of radiographic OA (Kellgren and Lawrence score), back disorders have the largest effect on WOMAC pain and physical functioning, and one of the largest effects on physical status of SF-36, besides obesity. Mental status was negatively influenced by the additional presence of duodenal ulcer, thyroid disease, and migraine or chronic headache. CONCLUSION: In early stage of OA, the presence of additional problems in the musculoskeletal system and of obesity have a negative effect on pain and physical health status. Also mental status is affected in early symptomatic OA by the presence of specific comorbidities. Comorbidity should be assessed and treated to improve the burden of illness in patients with early symptomatic OA.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".