MétaCan
Menu
Back to cohort
Record W7095349867

Printed in U.S.A Leukemia following Occupational Exposure to

2015· article· en· W7095349867 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldSocial Sciences
TopicTurkey's Politics and Society
Canadian institutionsnot available
Fundersnot available
KeywordsOdds ratioConfidence intervalConfoundingCohortLeukemiaMyeloid leukemiaOccupational exposureCancer
DOInot available

Abstract

fetched live from OpenAlex

In a nested case-control study of 1,484 cancer cases and 2,179 matched controls from a cohort of 31,543 Ontario Hydro male employees, the authors evaluated associations of cancer risk with electric field exposure and reevaluated the previously reported findings for magnetic fields. Pensioners were followed from January 1, 1970, and active workers (including those who left the corporation) from January 1,1973, with both groups followed through December 31,1988. Exposures to electric and magnetic fields and to potential occupational confounders were estimated through job exposure matrices. Odds ratios were elevated for hematopoietic malignancies with cumulative electric field exposure. After adjustment, the odds ratio for leukemia in the upper tertile was 4.45 (95 % confidence interval (Cl) 1.01-19.7). Odds ratios were also elevated for acute nonlymphoid leukemia, acute myeloid leukemia, and chronic lymphoid leukemia. For cumulative magnetic field exposure, there were similar elevations that fell with adjustment. Evaluation of the combined effect of electric and magnetic fields for leukemia showed significant elevations of risk for high exposure to both, with a dose-response relation for increasing exposure to electric fields and an inconsistent effect for magnetic fields. There was some evidence of a nonsignificant association for brain cancer and benign brain tumors with magnetic fields. For lung cancer, the odds ratio for high exposure to electric and magnetic fields was 1.84 (95 % Cl 0.69-4.94). Am J Epidemiol 1996; 144:150-60.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.503
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.059
GPT teacher head0.360
Teacher spread0.301 · 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 teacher head, 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
Published2015
Admission routes1
Has abstractyes

Explore more

Same topicTurkey's Politics and SocietyFrench-language works237,207