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Comparison of self-reported height and weight by cancer type among men from Montreal, Canada

2005· article· en· W2018277273 on OpenAlexaffabout
M-C Rousseau, M-É Parent, J Siemiatycki

Bibliographic record

VenueEuropean Journal of Cancer Prevention · 2005
Typearticle
Languageen
FieldMedicine
TopicEsophageal Cancer Research and Treatment
Canadian institutionsMcGill UniversityUniversité de MontréalInstitut National de la Recherche Scientifique
Fundersnot available
KeywordsMedicineCancerEpidemiologyBody mass indexAdenocarcinomaPopulationObesityDemographyInternal medicineKidney cancerOncologyEnvironmental health

Abstract

fetched live from OpenAlex

Among men there is epidemiological evidence for an association between obesity and increased risk of renal cell carcinoma, colon cancer and adenocarcinoma of the oesophagus. The evidence for other cancer sites remains inconsistent. We conducted a large population-based, multi-site, case-control study of environmental causes of cancer among males in Montreal, Canada. Among the many questionnaire items collected by interview were height and usual weight. We compared height, weight and body mass index (BMI) among individuals with 11 different cancer types (combined N=3016) and population-based controls (N=509). Linear regression was used to model the relationship of the disease status with each of three dependent continuous variables (height, weight and BMI), while adjusting for covariates. For most cancer groups, weight and BMI were lower than among population controls. Because of potential information bias and reverse causality bias, we focused on the comparisons among cancer types. The lowest BMI values were observed among men with squamous cell carcinoma of the oesophagus, lung and stomach cancers. The highest BMIs were reported by men with prostate and kidney cancers, and oesophageal adenocarcinoma. Inconsistencies in the epidemiological literature on obesity and cancer risk could be related to the difficulties in obtaining unbiased reports of pre-disease weight and to publication bias.

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)0.000
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.187
Threshold uncertainty score0.889

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.020
GPT teacher head0.331
Teacher spread0.311 · 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

Citations17
Published2005
Admission routes2
Has abstractyes

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