Mental disorders in a population sample with musculoskeletal disorders
Bibliographic record
Abstract
BACKGROUND: Studies using clinical and volunteer samples have reported an elevated prevalence of mood disorders in association with rheumatoid arthritis and osteoarthritis. Clinical studies using anxiety rating scales have reported inconsistent results, but studies using diagnostic instruments have reported that anxiety disorders may be even more strongly associated with arthritis than is depression. One study reported an association between lifetime substance use disorders and arthritis. METHODS: Data from iteration 1.2 of the Canadian Community Health Survey (CCHS) were used. This was a large-scale national Canadian health survey which administered the World Mental Health Composite International Diagnostic Interview to a sample of 36,984 subjects randomly selected from the national population. In the CCHS 1.2, subjects were asked whether they had been diagnosed by a health professional with arthritis or rheumatism. RESULTS: Subjects reporting arthritis or rheumatism had an elevated prevalence of mood, anxiety and substance use disorders. The strength of association resembled that seen in an omnibus category reporting any chronic condition, but was weaker than that seen with back pain or fibromyalgia. The effect of arthritis or rheumatism interacted with age, such that the odds ratios became smaller with increasing age. Mood and anxiety disorders, along with arthritis or rheumatism made an independent contribution to disability. CONCLUSION: Arthritis is associated with psychiatric morbidity in the general population, and this morbidity is seen across a variety of mental disorders. The strength of association is consistent with that seen in persons with other self-reported medical conditions.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".