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Record W3153059465 · doi:10.3390/curroncol28030147

Canadian Colorectal Cancer Screening Guidelines: Do They Need an Update Given Changing Incidence and Global Practice Patterns?

2021· review· en· W3153059465 on OpenAlexaffvenueabout
Anastasia Kalyta, Mary A. De Vera, Stuart Peacock, Jennifer J. Telford, Carl J. Brown, Fergal Donnellan, Sharlene Gill, Jonathan M. Loree

Bibliographic record

VenueCurrent Oncology · 2021
Typereview
Languageen
FieldMedicine
TopicColorectal Cancer Screening and Detection
Canadian institutionsSt. Paul's HospitalUniversity of British Columbia
Fundersnot available
KeywordsMedicineColorectal cancerIncidence (geometry)Health careColorectal cancer screeningDiseasePopulationContext (archaeology)Task forceCancerGerontologyCancer incidenceFamily medicineDemographyEnvironmental healthInternal medicineColonoscopy

Abstract

fetched live from OpenAlex

Colorectal cancer (CRC) is the third most commonly diagnosed cancer and second leading cause of cancer death in Canada. Organized screening programs targeting Canadians aged 50 to 74 at average risk of developing the disease have contributed to decreased rates of CRC, improved patient outcomes and reduced healthcare costs. However, data shows that recent incidence reductions are unique to the screening-age population, while rates in people under-50 are on the rise. Similar incidence patterns in the United States prompted the American Cancer Society and U.S. Preventive Services Task Force to recommend screening begin at age 45 rather than 50. We conducted a review of screening practices in Canada, framing them in the context of similar global health systems as well as the evidence supporting the recent U.S. recommendations. Epidemiologic changes in Canada suggest earlier screening initiation in average-risk individuals may be reasonable, but the balance of costs to benefits remains unclear.

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)0.008
metaresearch head score (Gemma)0.027
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.986
Threshold uncertainty score0.775

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.027
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0060.010
Science and technology studies0.0010.001
Scholarly communication0.0030.003
Open science0.0040.001
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0060.002

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 head0.149
GPT teacher head0.476
Teacher spread0.327 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations23
Published2021
Admission routes3
Has abstractyes

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