Environmental risk factors for amyotrophic lateral sclerosis: a case–control study in Canada and France
Bibliographic record
Abstract
Objective: To evaluate the association between amyotrophic lateral sclerosis (ALS) and potential environmental risk factors, especially head traumas and pesticides, in two populations from Canada and France. Methods: A case–control study was performed in tertiary-care centers. Consecutive ALS cases were recruited along with a control group from the same age distribution and region. Participants answered a phone-administered questionnaire. Head trauma exposure was censored at age of symptom onset, and a sensitivity analysis considering old head traumas that occurred more than 3 years before onset was performed. Univariate and multivariate logistic regression were used to estimate odds ratios (ORs) and their 95% confidence intervals (CIs). Results: A total of 404 ALS cases and 381 controls completed the questionnaire. Previous head trauma was more frequently reported by cases (adjusted OR 1.50 (1.05–2.18)) with a dose-response relationship. This association was driven by a strong effect in men (adjusted OR 2.06 (1.22–3.55)) and was consistent for old traumas, but there was no association in women. For pesticides, a previous high-risk occupation was associated with ALS (adjusted OR 2.08 (1.36–3.24)), although reported occupational exposure to pesticides was not statistically significant in the multivariate model (adjusted OR 1.67 (0.97–2.97)). Past electrocution was associated with ALS (adjusted OR 1.79 (CI 1.13–2.87)), especially spinal-onset ALS. Residential exposure to pesticides, neck trauma, and welding were not associated with ALS. Conclusions: Head trauma is a risk factor for ALS in men only. Previous occupation at high risk for pesticides exposure and electrocution are also risk factors for ALS.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".