Differences in the burden of psychiatric comorbidity in MS vs the general population
Bibliographic record
Abstract
OBJECTIVE: We aimed to compare the incidence and prevalence of psychiatric comorbidity in the multiple sclerosis (MS) population and in controls matched for age, sex, and geographic area. METHODS: Using population-based administrative health data from 4 Canadian provinces, we identified 2 cohorts: 44,452 persons with MS and 220,849 controls matched for age, sex, and geographic area. We applied validated case definitions to estimate the incidence and prevalence of depression, anxiety, bipolar disorder, and schizophrenia from 1995 to 2005. We pooled the results across provinces using meta-analyses. RESULTS: Of the MS cases, 31,757 (71.3%) were women with a mean (SD) age at the index date of 43.8 (13.7) years. In 2005, the annual incidence of depression per 100,000 persons with MS was 979 while the incidence of anxiety was 638, of bipolar disorder was 328, and of schizophrenia was 60. The incidence and prevalence estimates of all conditions were higher in the MS population than in the matched population. Although the incidence of depression was higher among women than men in both populations, the disparity in the incidence rates between the sexes was lower in the MS population (incidence rate ratio 1.26; 95% confidence interval: 1.07-1.49) than in the matched population (incidence rate ratio 1.50; 95% confidence interval: 1.21-1.86). Incidence rates were stable over time while prevalence increased slightly. CONCLUSIONS: Psychiatric comorbidity is common in MS, and more frequently affected the MS population than a matched population, although the incidence was stable over time. Men with MS face a disproportionately greater relative burden of depression when they develop MS than women.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".