Exposure to toxic occupations and their association with Parkinson’s disease: a systematic review with meta-analysis
Bibliographic record
Abstract
OBJECTIVES: Earlier longitudinal reviews on environmental and occupational toxins and Parkinson's disease (PD) risk have limitations. This study aimed to determine the strength of association between three types of toxic occupational exposures and the occurrence of PD by diagnostic methods. METHODS: A search was conducted of EMBASE, PubMed/Medline, Toxnet, LILACS, and Cochrane Library databases for longitudinal studies that assessed toxic occupational exposure, Parkinsonian, or related disorders, diagnosed by International Classification of Diseases (ICD) codes, medical records, or confirmation by a neurologist/nurse, and published in the English language from January 1990 to July 2021. Pooled risk ratios (RR) estimates were produced using random-effects models. Systematic review with meta-analysis synthesized the results. Study quality, heterogeneity, and publication bias were examined. High-quality articles that met the inclusion criteria were analyzed. RESULTS: Twenty-four articles were used in the analyses. The pooled RR for electromagnetic exposure and PD were (RR=1.03, 95% confidence interval [CI] 0.91-1.16) while the pooled RR between PD and metal and pesticide exposure were (RR=1.07, 95% CI 0.92-1.24) and (RR=1.41, 95% CI 1.20-1.65), respectively. Pooled RR for methods of diagnosis and their associations with PD were: confirmation by a neurologist or nurse (RR=2.17, 95% CI 1.32-3.54); ICD codes (RR=1.14, 95% CI 1.03-1.26), and medical records (RR=1.06, 95% CI 0.92-1.21). CONCLUSIONS: Our systematic review provides robust evidence that toxic occupational exposures are significant risk factors for PD especially those diagnosed by neurologists or nurses using standardized methods.
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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 | 0.016 | 0.041 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.046 |
| Bibliometrics | 0.009 | 0.010 |
| 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) | 0.003 | 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".