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Record W7095775292

Colorectal cancer testing in

2009· article· en· W7095775292 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMedicine
TopicColorectal Cancer Screening and Detection
Canadian institutionsnot available
Fundersnot available
KeywordsColorectal cancerSigmoidoscopyFecal occult bloodPopulationTest (biology)Multivariate analysisBivariate analysis
DOInot available

Abstract

fetched live from OpenAlex

Objectives This article provides estimates of the reported level of colorectal cancer (CRC) testing in the Canadian population aged 50 or older in 2008. Data sources and methods The data are from the 2008 Canadian Community Health Survey. With weighted data, the percentage of people who had undergone CRC testing (fecal occult blood test in the past two years or endoscopy within the past fi ve years) was estimated. Bivariate and multivariate analyses were used to examine testing status in relation to personal, socio-economic and other health-related characteristics. Results In 2008, an estimated 40 % of Canadians aged 50 or older reported that they had had CRC testing. The percentage ranged from 28 % in Quebec to 53 % in Manitoba. Testing was associated with being 65 or older, higher income, having a regular doctor, being a non-smoker, and being physically active. Interpretation Organized CRC screening was limited in 2008, but may account for some of the differences in participation among the provinces. Keywords colonoscopy, colorectal neoplasms, endoscopy, fecal occult blood test, mass screening, sigmoidoscopy

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)0.003
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: none
Teacher disagreement score0.615
Threshold uncertainty score0.775

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0190.002

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 head0.032
GPT teacher head0.308
Teacher spread0.276 · 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
Published2009
Admission routes1
Has abstractyes

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