Disability worsening among persons with multiple sclerosis and depression
Bibliographic record
Abstract
OBJECTIVE: Depression is common in multiple sclerosis (MS), but its impact on disability worsening has not yet been determined. We explored the risk of disability worsening associated with depression in a nationwide longitudinal cohort. METHODS: This retrospective cohort study used linked data from 3 Swedish nationwide registries: the MS Register, National Patient Register, and Prescribed Drug Register. Two incident cohorts were developed: cohort 1 included all registered cases of MS in the MS Registry (2001-2014) with depression defined as ≥1 ICD-10 code for depression; and cohort 2 comprised all cases of MS in the MS Registry (2005-2014) with depression defined as ≥1 prescription filled for an antidepressant. Cox regression models were used to compare the risk of reaching sustained disability milestone scores of 3.0, 4.0, and 6.0 on the Expanded Disability Status Scale (EDSS) between persons with MS with and without depression. RESULTS: Cohort 1 included 5,875 cases; 502 (8.5%) had depression. Cohort 2 had 3,817 cases; 1,289 (33.8%) were prescribed an antidepressant. Persons with depression were at a significantly higher risk of reaching sustained EDSS scores of 3.0, 4.0, and 6.0, with hazard ratios of 1.50 (95% confidence interval [CI] 1.20-1.87), 1.79 (95% CI 1.40-2.29), and 1.89 (95% CI 1.38-2.57), respectively. A similar increased risk among persons exposed to antidepressants was observed, with hazard ratios of 1.37 (95% CI 1.18-1.60), 1.93 (95% CI 1.61-2.31), and 1.86 (95% CI 1.45-2.40) for sustained EDSS scores of 3.0, 4.0, and 6.0, respectively. CONCLUSION: Persons with MS and comorbid depression had a significantly increased risk of disability worsening. This finding highlights the need for early recognition and appropriate treatment of depression in persons with MS.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".