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Record W2485350334 · doi:10.18192/riss-ijhs.v2i1.1524

The Risk of Prostate Cancer from Occupational Exposures in Male Firefighters

2011· article· en· W2485350334 on OpenAlexaffvenue
Zing-Wae Wong, James Gomes

Bibliographic record

VenueRevue interdisciplinaire des sciences de la santé - Interdisciplinary Journal of Health Sciences · 2011
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Health and Performance
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsProstate cancerConfoundingMedicineFirefightingCancerEnvironmental healthProstateOncologyInternal medicineGeography

Abstract

fetched live from OpenAlex

Prostate cancer is the most commonly diagnosed cancer among men. Since prostate cancer is a slow developing cancer, mortality can be prevented if the tumour is detected and treated in its early stages. There is proof that environmental exposures can increase the risk of prostate cancer. Many papers have performed data analyses on prostate cancer levels in firefighters. There has been some research on firefighter prostate cancer levels but few reviews on the topic. This paper focuses on finding whether there is a correlation between firefighting occupation and levels of prostate cancer. As well, this paper notes potential carcinogens within the firefighting occupation. Five papers were included in this review; these papers used different methods to obtain the cases and cohorts for the study. The papers also used different controls for comparison. Nevertheless, the papers found in the search supported a positive correlation between exposures in firefighting occupations and the level of prostate cancer. The two chemicals that were suspected carcinogens in these studies were Polyaromatic hydrocarbons (PAH) and fire smoke. However, future research can be more rigorous in calculations of prostate cancer levels by including lifestyle factors, other confounders such as smoking, and estimated length of exposure.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.016
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.050
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0160.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0060.005
Scholarly communication0.0000.001
Open science0.0020.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.107
GPT teacher head0.498
Teacher spread0.391 · 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; both teacher heads agree on what is shown here.

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
Published2011
Admission routes2
Has abstractyes

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