Depression in multiple sclerosis: A long-term longitudinal study
Bibliographic record
Abstract
BACKGROUND: Depression is a common comorbidity in multiple sclerosis (MS), but little is known about its long-term prognosis. Depression in the general population is usually episodic with relatively short-lasting depressive episodes. In this study we investigate the long-term prognosis of depression in MS. METHODS: Using data from a large longitudinal observational study and from the Calgary MS clinic database, we investigated changes in Center for Epidemiological Studies Depression Scale (CESD) scores in MS patients over four years of follow-up. We used logistic regression to investigate the association of the factors sex, age, disease duration, Expanded Disability Status Scale (EDSS), depression at baseline, and antidepressant use with depression at each year of follow-up. RESULTS: CESD scores remained largely stable, or decreased slightly over four years of follow-up, whereas EDSS scores steadily increased. Depression at baseline was the strongest predictor of depression at follow-up; the other factors were not or not consistently associated with depression at follow-up. As expected, antidepressant use was associated with a greater risk of depression at follow-up. Starting and stopping antidepressant treatment during follow-up was not associated with the risk of depression at follow-up or with significant change in CESD scores. CONCLUSION: In contrast to depression in the general population, depression in MS is largely chronic, which suggests a different pathophysiology.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".