Drug exposure and the risk of multiple sclerosis: A systematic review
Bibliographic record
Abstract
BACKGROUND: Several environmental and lifestyle factors have been associated with multiple sclerosis (MS) risk, including some pharmacological treatments. We systematically reviewed the literature on prescription drug exposure and MS risk. METHODS: Six databases were searched for original observational studies reporting drug exposure and MS risk published before 2017. RESULTS: Thirteen articles fulfilled inclusion criteria. Exposure to neither amiloride nor valproic acid was associated with MS (adjusted hazard ratio (adj.HR = 1.34;95% CI:0.81-2.20; adj.HR = 1.30;95%CI:0.44-3.80, respectively). Four studies explored oral contraceptive exposure and reported no association with MS; while a single study found an increased risk (odds ratio [adj.OR] = 1.52;95%CI:1.21-1.91). While penicillin exposure was associated with reduced risk of developing MS (adj.OR = 0.5;95%CI:0.3-0.9), a later study observed an elevated risk for penicillin (adj.OR = 1.21;95%CI:1.10-1.27) and all antibiotics (adj.OR = 1.41;95%CI:1.29-1.53), which was potentially attributed to underlying infection. Anti-tumor necrosis factor-alpha (TNFα) was not associated with MS risk in persons with inflammatory bowel disease (standard morbidity ratio = 4.2;95%CI:0.1-23.0) and arthritis (standardized incidence ratio = 1.38;95%CI:0.69-2.77); however, men exposed to anti-TNFα who also had arthritis and individuals with ankylosing spondylitis were at an increased risk (standardized incidence ratios = 3.91;95%CI:1.47-10.42 and 3.48;95%CI:1.45-8.37, respectively). A reduced risk of MS was observed with exposure to the beta2-adrenergic agonist fenoterol (adj.OR = 0.58;95%CI:0.45-0.76), and the sedating histamine 1-receptor antagonists (adj.OR = 0.2;95%CI:0.1-0.8), but not the non-sedating equivalent (adj.OR = 0.8;95%CI:0.4-1.6). CONCLUSIONS: The suggestion that some drugs may prevent MS is intriguing and warrants further study. In addition, further pharmacovigilance is needed to assess the safety of anti-TNFα drugs in specific populations in the context of MS risk.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.009 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".