Depressive symptoms in a treated multiple sclerosis cohort
Bibliographic record
Abstract
BACKGROUND: Recent side effect data from clinical trials of interferon beta in multiple sclerosis (MS) have failed to confirm that these medications are associated with an increased risk of depression. However, these studies have used highly selected samples and the results may not be generalizable to real world settings. METHODS: Clinical data on subjects from southern Alberta who have applied for, or are receiving, public reimbursement for MS treatment are maintained in a database at the University of Calgary Multiple Sclerosis Clinic. Depression ratings obtained using the Center for Epidemiological Studies Depression Rating Scale (CES-D) are included in this database. In the current analysis, these longitudinal data were used to determine whether depressive symptoms were associated with disease-modifying treatments. RESULTS: At baseline, ratings were available for 163 subjects. Those choosing interferon beta resembled those choosing glatiramer acetate in most respects. During follow-up, no differences were observed in the prevalence or incidence of depression and CES-D scores were not found to differ between the treatment groups. CONCLUSIONS: The failure to identify higher rates of depression both in previous intervention studies and in the current observational study provides confirmation that these drugs are not substantially associated with the occurrence of depression.
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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 | 0.001 |
| 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 | 0.000 |
| 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".