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Record W2332048425 · doi:10.1158/1538-7445.am2011-1914

Abstract 1914: Consumption of Lake Ontario sport fish and prostate cancer incidence in the New York State Angler Cohort Study (NYSACS)

2011· article· en· W2332048425 on OpenAlexaboutno aff
Joseph W. Green, Lina Mu, Mya Swanson, Jacqueline Mix, Matthew R. Bonner

Bibliographic record

VenueCancer Research · 2011
Typearticle
Languageen
FieldMedicine
TopicNutritional Studies and Diet
Canadian institutionsnot available
Fundersnot available
KeywordsMedicinePoisson regressionCohortProstate cancerCancer registryProspective cohort studyDemographyEnvironmental healthConfoundingIncidence (geometry)Cohort studyConfidence intervalCancerGerontologyInternal medicinePopulation

Abstract

fetched live from OpenAlex

Abstract Fish from the Great Lakes are an important source of exposure to persistent organic pollutants, including polychlorinated biphenyls (PCBs) and organochlorine pesticides (e.g., Mirex and DDT). Some of these pollutants are hypothesized to be hormonally active and therefore may increase the risk of prostate cancer. Conversely, fish are also a source of micro- and macro-nutrients hypothesized to have chemopreventive properties. We investigated the consumption of sport-caught fish from Lake Ontario and the incidence of prostate cancer in the NYSACS, a prospective cohort of 17,110 anglers and their spouses aged 18 to 40 years old at enrollment. Participants completed a mailed self-administered questionnaire upon enrollment in 1991. Demographic factors, smoking history and selected potential confounders were ascertained. The questionnaire also queried for the number of years that fish from Lakes Ontario and Erie were consumed as well as the preparation and cooking practices for sport caught fish. As of December 31, 2008 fifty-eight first primary incident prostate cancers were identified via the New York State Cancer Registry. Vital status was determined by linkage with the Social Security Administration Death file. Of the 10,436 males enrolled at baseline, 10% (n=1,075) were lost to follow-up. Fish consumption was dichotomized into never vs. ever. Duration of consumption was categorized into tertiles based on the distribution among the cases who reported eating L Ontario sport caught fish. Poisson regression was used to calculate rate ratios (RR) and 95% confidence intervals, adjusting for age, education and pack-years of smoking. Ever eating Lake Ontario fish was inversely associated with prostate cancer incidence (RRadjusted=0.5 (95% CI=0.3-0.9)) compared with never consumers. In addition, we found a suggestion of an inverse association with increasing duration of L. Ontario fish consumption (non-eaters RR = 1.0 (ref); 1st tertile RR = 0.6 (95% CI = 0.3-1.3); 2nd tertile RR = 0.4 (95% CI = 0.2-1.0); 3rd tertile RR = 0.5 (95% CI = 0.2-1.0); although the exposure-response gradient was neither monotonic nor significant (ptrend = 0.157). The exposure-response gradients were similar when consumption was lagged 5- and 10-years (5-year lag ptrend = 0.289; 10-year ptrend = 0.269). While interpretation is complicated by a lack of information on family history of prostate cancer, and a small number of cases, these preliminary results are suggestive of an inverse association between Lake Ontario fish consumption and prostate cancer risk. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr 1914. doi:10.1158/1538-7445.AM2011-1914

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)0.000
metaresearch head score (Gemma)0.000
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 score0.665
Threshold uncertainty score0.675

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.148
GPT teacher head0.397
Teacher spread0.248 · 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
Published2011
Admission routes1
Has abstractyes

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