Medication use and risk of amyotrophic lateral sclerosis—a systematic review
Bibliographic record
Abstract
Abstract Background Studying whether medications act as potential risk factors for amyotrophic lateral sclerosis (ALS) can contribute to the understanding of disease etiology as well as the identification of novel therapeutic targets. Therefore, we conducted a systematic review to summarize the existing evidence on the association between medication use and the subsequent ALS risk. Methods A systematic review was conducted in Medline, Embase, and Web of Science from the date of database establishment to December 10, 2021. References of identified articles were further searched for additional relevant articles. Studies were included if (1) published in English, (2) explored medication use as exposure and development of ALS as outcome, and (3) the design was a human observational study. Clinical trials, reviews, comments, editorials, and case reports were excluded. Quality assessment was performed using a pre-validated tool for non-randomized studies, the Newcastle–Ottawa Assessment Scale (NOS). Results Of the 4760 studies identified, 25 articles, including 13 case–control studies, five nested case–control studies, six cohort studies, and one retrospective chart review, were included in the review. Among these studies, there were 22 distinct study populations that included 171,407 patients with ALS, seven classes of medication examined, and 23 studies with a NOS ≥ 5. There was a general lack of agreement between studies on the associations of cholesterol-lowering drugs, anti-inflammatory drugs, immunosuppressants, antibiotics, oral contraceptives (OCs) or hormone replacement therapy (HRT), antihypertensive drugs, antidiabetics, and drugs for psychiatric and neurological disorders with the subsequent risk of ALS. However, it appeared that statins, aspirin, OCs/HRT, antihypertensives, and antidiabetics were unlikely related to a higher risk of ALS. The positive associations noted for antibiotics, antidepressants, and skeletal muscle relaxants might be attributable to prodromal symptoms of ALS. Conclusions There is currently no strong evidence to link any medication use with ALS 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 | 0.006 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".