Bibliographic record
Abstract
Neurodegenerative diseases are a group of progressive neurological disorders with continuously rising incidence rates. Aluminum, diesel engine exhaust, and radon have been studied as risk factors for neurodegenerative diseases, but no research has been performed on populations of miners with high levels of exposure. This dissertation used one mining cohort and one occupational disease surveillance cohort from Ontario, Canada, to understand the patterns of neurodegenerative diseases among miners and to estimate the association of mine type, aluminum dust, diesel engine exhaust, and radon with risk of neurodegenerative diseases (Alzheimer’s disease, Alzheimer’s with other dementias, Parkinson’s disease, parkinsonism, and motor neuron disease). Two Ontario cohorts were used for my analysis, one subset of over 1.2 million workers from a linked surveillance cohort, the Occupational Disease Surveillance System (ODSS), and the other linked cohort of 36,836 Ontario miners, the Mining Master File (MMF). Poisson regression models were used to examine incidence rate ratios of different neurodegenerative outcomes. McIntyre Powder exposure was assessed using both cleaned self-reports and reconstruction of powder use from historical records. Radon exposure assessed using job-exposure matrices that were developed using results of radon measurements carried out in Ontario mines. Different exposure assessment approaches were explored for diesel exhaust exposure, including a mine-based diesel equipment use indicator and reconstructions of diesel use for underground mines through historical records and expert assessments. In the ODSS cohort, an elevated incidence rate of motor neuron disease was suggested among workers in metal mines, as well as an indicative elevation of Alzheimer's or Parkinson's disease rates among workers of gold and miscellaneous metal (primarily nickel-copper ore) mines. In the MMF cohort, I observed a 30% increased rate of Parkinson's disease and 10% increased rate of Alzheimer's with other dementia in association with respirable aluminum dust exposure. However, my findings do not support positive associations between cumulative radon exposure level and diesel exhaust exposure duration and risk of neurodegenerative outcomes. My findings support aluminum as a risk factor for Parkinson’s disease, yet more epidemiological research is needed to understand the role of aluminum, radon, and diesel exhaust in the development of neurodegenerative outcomes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".