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Record W3110571194 · doi:10.1038/s41598-020-76294-w

Combinations of modifiable lifestyle behaviours in relation to colorectal cancer risk in Alberta’s Tomorrow Project

2020· article· en· W3110571194 on OpenAlexafffundabout
Dylan E. O’Sullivan, Amy Metcalfe, Troy Hillier, Will D. King, Sang‐Min Lee, Joy Pader, Darren R. Brenner

Bibliographic record

VenueScientific Reports · 2020
Typearticle
Languageen
FieldMedicine
TopicColorectal Cancer Screening and Detection
Canadian institutionsUniversity of CalgaryAlberta Health ServicesQueen's University
FundersHealth CanadaAlberta InnovatesCanadian Institutes of Health ResearchAlberta Cancer FoundationPartenariat Canadien Contre Le CancerAlberta Health Services
KeywordsHazard ratioColorectal cancerMedicineProportional hazards modelEnvironmental healthObesityPopulationConsumption (sociology)DemographyAlcohol consumptionGerontologyCancerInternal medicineConfidence intervalAlcoholBiology

Abstract

fetched live from OpenAlex

The objective of this study was to identify distinct clusters of individuals that exhibit unique patterns of modifiable lifestyle-related behaviours and to determine how these patterns are associated with the risk of developing colorectal cancer (CRC). The study consisted of 26,460 participants and 267 CRC cases from Alberta's Tomorrow Project. Exploratory latent class analysis of risk behaviours (obesity, physical inactivity, meat consumption, smoking, alcohol consumption, and fruit and vegetable consumption) and Cox proportional hazard models were utilized. Seven unique behavioural groups were identified, where the risk of CRC was 2.34 to 2.87 times greater for high risk groups compared to the low risk group. Sex-specific models identified higher risk groups among men (Hazard Ratios [HRs]: 3.15 to 3.89) than among women (HRs: 1.99 to 2.19). Targeting groups defined by clustering of behaviours could potentially lead to more effective prevention of CRC on a population level.

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)0.001
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 score0.067
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
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 head0.023
GPT teacher head0.292
Teacher spread0.269 · 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

Citations19
Published2020
Admission routes3
Has abstractyes

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