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Record W2054799761 · doi:10.1179/107735207800244992

Cancer and Construction: What Occupational Historiesina Canadian Community Reveal

2007· article· en· W2054799761 on OpenAlexaboutno aff
James T. Brophy, Margaret M. Keith, Kevin M. Gorey, Ethan Laukkanen, Isaac Luginaah, Hakam Abu-Zahra, Andrew Watterson, Deborah Hellyer, Abraham Reinhartz, Robert M. Park

Bibliographic record

VenueInternational Journal of Occupational and Environmental Health · 2007
Typearticle
Languageen
FieldMedicine
TopicOccupational and environmental lung diseases
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineOdds ratioPopulationCancerEnvironmental healthDemographyOccupational medicineOccupational exposureInternal medicine

Abstract

fetched live from OpenAlex

From 2000 to 2002, male patients at a Canadian cancer treatment center with new-incident head-and-neck or esophageal cancers were invited to participate in a population-based study. The study population included 87 cases and 172 controls. A lifetime-history questionnaire was administered. Odds ratios (ORs) were calculated for occupational groups with a minimum of five cases, adjusted for duration of employment, age, smoking, alcohol, education, and income. A significantly increased risk was shown for construction workers (OR = 2.20; 95% CI 1.25-3.91). This investigation of a set of rare cancers over a limited time period demonstrates the feasibility of this research approach. The increased risk among construction workers supports the need for more comprehensive study of exposures in this occupational group.

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)0.003
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 score0.025
Threshold uncertainty score0.055

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.006
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.028
GPT teacher head0.331
Teacher spread0.303 · 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

Citations5
Published2007
Admission routes1
Has abstractyes

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