Comorbidities in Osteoarthritis: A Systematic Review and Meta‐Analysis of Observational Studies
Bibliographic record
Abstract
OBJECTIVE: Osteoarthritis (OA) is a common chronic condition in older individuals, but its association with other chronic conditions is largely unknown. This study aimed to systematically review the literature on comorbidities in individuals with OA compared to those without. METHODS: We searched 4 databases for observational studies on comorbidities in individuals with OA. Studies of OA only or in comparison with non-OA controls were included. The risk of bias and study quality were assessed using the Newcastle-Ottawa Scale. The prevalence of comorbidities in the OA group and the prevalence ratio (PR) and 95% confidence interval (95% CI) between OA and non-OA groups were calculated. RESULTS: In all, 42 studies from 16 countries (27 case-only and 15 comparative studies) met the inclusion criteria. The mean age of participants varied from 51 to 76 years. The pooled prevalence of any comorbidity was 67% (95% CI 57-74) in individuals with OA versus 56% (95% CI 44-68) in individuals without OA. The pooled PR for any comorbidity was 1.21 (95% CI 1.02-1.45). The PR increased from 0.73 (95% CI 0.43-1.25) for 1 comorbidity to 1.58 (95% CI 1.03-2.42) for 2, and to 1.94 (95% CI 1.45-2.59) for ≥3 comorbidities. The key comorbidities associated with OA were stroke (PR 2.61 [95% CI 2.13-3.21]), peptic ulcer (PR 2.36 [95% CI 1.71-3.27]), and metabolic syndrome (PR 1.94 [95% CI 1.21-3.12]). CONCLUSION: Individuals with OA are more likely to have other chronic conditions. The association is dose-dependent in terms of the number of comorbidities, suggesting multimorbidities. Further studies on the causality of this association and clinical implications are needed.
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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 | 0.017 | 0.045 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.032 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".