Estimation of the marginal effect of regular drug use on multiple sclerosis in the Iranian population
Bibliographic record
Abstract
There are only few reports regarding the role of lifetime drug or substance use in multiple sclerosis (MS) etiology. In this study, we investigated the potential effect of drug or substance exposure on the onset of MS diagnosis. We conducted a population-based incident case control study in Tehran. Cases (n = 547) were 15-50 years old persons with MS identified from the Iranian Multiple Sclerosis Society (IMSS) register during August 7, 2013, and November 17, 2015. Population-based controls (n = 1057) were 15-50 years old and were recruited by random digit telephone dialing. Inverse-probability-of-treatment weighing (IPTW) using two sets of propensity scores (PSs) was used to estimate marginal incidence odds ratios (ORs) for MS contrasting pre-specified substance use. The estimated marginal OR was 6.03 (95% confidence interval: 3.54;10.3, using trimmed weights at the 95th percentile of the stabilized weight distribution) in both IPTW analyses comparing lifetime substance use (opioids, cannabis, inhalants, hallucinogens and stimulants) for at least one time monthly during a six-months or longer period vs. no such history of drug use. Subject to limitation of causal claims based on case-control studies, this study suggests that monthly drug or substance use for a period of at least six consecutive months, may increase the risk of MS by factor 3.5 or higher.
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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 | 0.013 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".