Bibliographic record
Abstract
Background and aims: Active smoking is a known risk factor for Multiple sclerosis (MS) development and poor prognosis. However, the impact of past exposure to parental smoking (ParS), including maternal smoking during pregnancy (MSDP) is not well defined. We aimed to investigate how these types of early age exposures affect MS risk among adults. Methods: Using the data collected by the EnvIMS study, a large multinational case–control population-based study, we investigated the association between MS and smoking habit, MSDP and maternal/paternal smoking (MaS, PaS) in Canadian, Italian, and Norwegian populations. Data were collected with EnvIMS-Q, designed to investigate environmental exposures during early life stages. Adjusted odds ratios (aOR) for index age and participants' smoking status are presented with 95% confidence intervals (95% CI). Results: We included 1565 Canadian, 2040 Italian, and 2674 Norwegian subjects. An association between MS and MSDP and MaS was observed among Norwegians: aOR 1.38 (1.12, 1.71) and 1.39 (1.17, 1.65), respectively. A tendency for PaS to be associated with MS was found among Canadians: aOR 1.21 (0.97, 1.51). No significant association to ParS (any) was detected in the Italian population. Conclusion: Selective exposure to ParS at early age may differentially increase MS risk in the general population and independently from the subject's past/current smoking habit. The developmental origin of health and disease (‘DOHaD’) theory may help interpret these findings. The absence of an association between MS and past exposure to ParS in other populations may reflect its smaller effect on MS risk compared to other factors.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.693 | 0.485 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".