Association between the use of selective serotonin reuptake inhibitors and multiple sclerosis disability progression
Bibliographic record
Abstract
BACKGROUND: Benefits of selective serotonin reuptake inhibitors (SSRIs) in modifying the multiple sclerosis (MS) disease course have been suggested, but their ability to delay disability progression remains unknown. We examined the association between SSRI exposure and MS disability progression. METHODS: A nested case-control study was conducted using the British Columbia (Canada) Multiple Sclerosis clinical data linked to health administrative data. The primary outcome was a sustained score of 6 (requires a cane to walk) on the Expanded Disability Status Scale (EDSS), and the secondary outcome was the onset of secondary progressive MS (SPMS, an advanced stage of MS). The cases were those who reached a study outcome and were matched with up to four randomly selected controls by sex, age, EDSS and calendar year at study entry using incidence density sampling. The associations between disability worsening and SSRI exposure were assessed with conditional logistic regression models, adjusted for confounders. RESULTS: A total of 3920 patients were included in the main analyses, of which 272 reached sustained EDSS 6 and 187 reached SPMS. SSRI exposure was significantly different between patients who reached sustained EDSS 6 and controls [adjusted odds ratio (adjOR):1.44; 95% confidence interval (CI):1.03-2.01]. However, SSRI exposure was not significantly different between those who reached SPMS and their controls (adjOR:1.35; 95%CI:0.89-2.04). CONCLUSION: We found no evidence to suggest that SSRI exposure was associated with a delay in MS disability accumulation or progression. Copyright © 2016 John Wiley & Sons, Ltd.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".