Association of Parkinson's disease with infections and occupational exposure to possible vectors
Bibliographic record
Abstract
The ultimate causes of idiopathic Parkinson's disease (PD) are not fully known, but environmental and occupational causes are suspected. Postencephalitic parkinsonism has been linked to influenza, and other viral infections have also been suspected to relate to PD. We estimated the relationship between PD and both infections and possible vectors of infection (i.e., animal and human) in a population-based, case-control study in British Columbia, Canada. We recruited 403 cases detected by their use of antiparkinsonian medications and 405 controls from the registrants of the provincial universal health insurance plan. Severe influenza was associated with PD (odds ratio [OR]: 2.01; 95% confidence interval [CI]: 1.16-3.48), although this effect was attenuated when reports were restricted to those occurring 10 or more years before diagnosis. Childhood illnesses were inversely associated with PD, particularly red measles (OR: 0.65; 95% CI: 0.48-0.90). Several animal exposures were associated with PD, with statistically significant effects for cats (OR: 2.06; 95% CI: 1.09-3.92) and cattle (OR: 2.23; 95% CI: 1.22-4.09). Influenza infection may be associated with PD. The inverse relationships with childhood infections may suggest an increased risk with subclinical or asymptomatic childhood infections. Occupational exposure to animals may increase risk through transmission of infections or may indicate exposure to another agent of interest (e.g., bacterial endotoxin).
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".