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Record W2810191107 · doi:10.3747/co.25.4071

The North–South and East–West Gradient in Colorectal Cancer Risk: A Look at the Distribution of Modifiable Risk Factors and Incidence across Canada

2018· article· en· W2810191107 on OpenAlexaffvenueabout
J. Tung, Chris Politis, J. Chadder, Jing Han, Jin Niu, S. Fung, Rami Rahal, Craig C. Earle

Bibliographic record

VenueCurrent Oncology · 2018
Typearticle
Languageen
FieldMedicine
TopicColorectal Cancer Screening and Detection
Canadian institutionsCanadian Partnership Against Cancer
Fundersnot available
KeywordsMedicineColorectal cancerEnvironmental healthIncidence (geometry)PopulationConsumption (sociology)JurisdictionPublic healthDemographyCancerDistribution (mathematics)GerontologyPathologyInternal medicine

Abstract

fetched live from OpenAlex

Colorectal cancer (crc) is the 2nd most common cancer in Canada and the 2nd leading cause of cancer death. That heavy burden can be mitigated given the preventability of crc through lifestyle changes and screening. Here, we describe the extent of the variation in crc incidence rates across Canada and the disparities, by jurisdiction, in the prevalence of modifiable risk factors known to contribute to the crc burden. Findings suggest that there is a north-south and east-west gradient in crc modifiable risk factors, including excess weight, physical inactivity, excessive alcohol consumption, and low fruit and vegetable consumption, with the highest prevalence of risk factors typically found in the territories and Atlantic provinces. In general, that pattern reflects the crc incidence rates seen across Canada. Given the substantial interjurisdictional variation, more work is needed to increase prevention efforts, including promoting a healthier diet and lifestyle, especially in jurisdictions facing disproportionately higher burdens of crc. Based on current knowledge, the most effective approaches to reduce the burden of crc include adopting public policies that create healthier environments in which people live, work, learn, and play; making healthy choices easier; and continuing to emphasize screening and early detection. Strategic approaches to modifiable risk factors and mechanisms for early cancer detection have the potential to translate into positive effects for population health and fewer Canadians developing and dying from cancer.

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 score0.435
Threshold uncertainty score0.473

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 studies0.0000.001
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 head0.035
GPT teacher head0.328
Teacher spread0.292 · 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

Citations10
Published2018
Admission routes3
Has abstractyes

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