Original Article Occupations and Parkinson’s Disease: A Case-Control Study in South Korea
Bibliographic record
Abstract
Abstract: We performed a hospital based case-control study in the southeast region of Korea to clarify the role of occupational exposure, especially manganese (Mn), in the etiology of Parkinson’s disease (PD) and to discover the association between any occupation and PD. 105 outpatients with PD and 129 neurological disease controls and 101 healthy controls were interviewed. We employed occupational and industrial categories as defined by Section (the most broad category) and Division (sub-category) of the Korea Standard Industry Code and the Korea Standard Classification of Occupations. There was not a significant association between exposure to hazardous materials, especially Mn and PD. There were not any occupations listed under the Section of Industry Classification as a significant risk factor or protective factor for PD. However, the ‘clerk ’ occupation [Section] was positively associated with PD. There is a decreased risk for PD with a subject ever having worked in the ‘agriculture, forestry and fishery ’ occupational group. Ever having worked in ‘sales ’ also was negatively associated with PD. There were not any Divisions of Industry found as a significant risk factor or protective factor for PD. However, ever having worked in an ‘agriculture’ Division of Occupation was negatively associated with PD.
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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) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".