Paraquat and Parkinson’s disease: a systematic review and meta-analysis of observational studies
Bibliographic record
Abstract
This investigation aimed to conduct a systematic review of the literature and meta-analysis to determine whether exposure to the herbicide paraquat was associated with the development of Parkinson’s disease (PD). Observational studies that enrolled adults exposed to paraquat with PD as the outcome of interest were searched in the PubMed, Embase, LILACS, TOXNET, and Web of Science databases up to May 2019. Two authors independently selected relevant studies, extracted data, and assessed methodological quality. The evidence certainty was assessed by the GRADE approach, which served as basis for a tentative causality assessment, supplemented by the Bradford Hill criteria when necessary. Results from nine case–control studies indicated that PD occurrence was 25% higher in participants exposed to paraquat. The only cohort investigation included demonstrated a non-significant OR of 1.08. Results from subgroup analyses also indicated higher PD frequency in participants that were exposed to paraquat for longer periods or individuals co-exposed with paraquat and any other dithiocarbamate. Data indicate apositive association between exposure to paraquat and PD occurrence, but the weight-of-evidence does not enable one to assume an indisputable cause–effect relationship between these two conditions. Better designed studies are needed to increase confidence in results.Systematic Review Registration: PROSPERO CRD42017069994.
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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 | 0.020 | 0.049 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.026 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".