Rural environment, pesticide exposure and the risk of amyotrophic lateral sclerosis: a meta analysis
Bibliographic record
Abstract
Objective To explore the relationship between the risk of amyotrophic lateral sclerosis (ALS) and exposure to rural environments and pesticide. Methods Studies relevant to rural residence, farmer occupation, pesticide exposure and ALS were identified from the databases including Embase, Ovid Medline, Pubmed, Cochrane Library, Wanfang data, Chinese BioMedical Literature Database, Chinese National Knowledge Infrastructure, China Science and Technology Journal Database up to March 2015.Quality of studies was assessed according to the Newcastle-Ottawa Scale (NOS). Analysis of data and publication bias was performed with software Revman 5.3. Results A total of 24 case-control studies and 3 cohort studies were included into the analysis.The NOS scores of all studies were ≥6. The risk of ALS was associated with pesticide exposure (OR=1.41, 95% CI 1.28-1.56) and farmer occupation (OR=1.42, 95% CI 1.29-1.57), but not associated with rural residence (OR=1.21, 95% CI 0.97-1.51). Subgroup analysis of pesticide exposure and ALS revealed that males (OR=1.75, 95% CI 1.39-2.21) had a higher risk than females (OR=1.53, 95% CI 1.13-2.08), and the risk estimate was higher in studies using El Escorial standard (OR=1.68, 95% CI 1.45-1.95) than studies not (OR=1.23, 95% CI 1.08-1.40). The meta analysis had a slight publication bias. Conclusions Our findings support pesticide exposure might increase the risk of ALS. Given that farmers always have high levels of pesticide exposure in their work, they should decrease their exposure level or take proper precautions to lower the risk of ALS. Key words: Amyotrophic lateral sclerosis; Pesticide residues; Risk factors; Environmental exposure; Meta-analysis
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 | 0.021 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.036 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".