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Record W7133026631

Neurodegenerative Diseases in Two Ontario Mining Cohorts

2022· dissertation· W7133026631 on OpenAlexaboutno aff
Xiaoke Zeng

Bibliographic record

VenueTSpace · 2022
Typedissertation
Language
FieldAgricultural and Biological Sciences
TopicAluminum toxicity and tolerance in plants and animals
Canadian institutionsnot available
Fundersnot available
KeywordsCohortPoisson regressionDiseaseIncidence (geometry)RadonDiesel fuelCohort study
DOInot available

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.046
Threshold uncertainty score0.097

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.003
Science and technology studies0.0030.001
Scholarly communication0.0010.000
Open science0.0010.002
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.029
GPT teacher head0.316
Teacher spread0.287 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2022
Admission routes1
Has abstractyes

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