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Record W114695904 · doi:10.1155/2007/938978

Do Physician Recommendations for Colorectal Cancer Screening Differ by Patient Age?

2007· article· en· W114695904 on OpenAlexaffvenue
Maida Sewitch, Caroline Fournier, Martin Dawes, Mark J. Yaffe⃰, Linda Snell, Mark Roper, Patrizia Zanelli, Alan Pavilanis

Bibliographic record

VenueCanadian Journal of Gastroenterology · 2007
Typearticle
Languageen
FieldMedicine
TopicColorectal Cancer Screening and Detection
Canadian institutionsMcGill University Health CentreMcGill University
Fundersnot available
KeywordsMedicineColonoscopyFecal occult bloodColorectal cancerComorbidityColorectal cancer screeningMarital statusCancerInternal medicineCancer screeningPopulation

Abstract

fetched live from OpenAlex

Colorectal cancer screening is underutilized, resulting in preventable morbidity and mortality. In the present study, age-related and other disparities associated with physicians' delivery of colorectal cancer screening recommendations were examined. The present cross-sectional study included 43 physicians and 618 of their patients, aged 50 to 80 years, without past or present colorectal cancer. Of the 285 screen-eligible patients, 45% received a recommendation. Multivariate analyses revealed that, compared with younger nondepressed patients, older depressed patients were less likely to receive fecal occult blood test recommendations, compared with no recommendation (OR=0.31, 95% CI 0.09 to 1.02), as well as less likely to receive colonoscopy recommendations, compared with no recommendation (OR=0.14; 95% CI 0.03 to 0.66). Comorbidity and marital status were associated with delivery of fecal occult blood test and colonoscopy recommendations, respectively, compared with no recommendation. In summary, patient age and other characteristics appeared to influence physicians' delivery of colorectal cancer screening and choice of modality.

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)0.003
metaresearch head score (Gemma)0.032
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: Empirical
Teacher disagreement score0.978
Threshold uncertainty score0.044

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.032
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.280
Teacher spread0.260 · 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

Citations26
Published2007
Admission routes2
Has abstractyes

Explore more

Same venueCanadian Journal of GastroenterologySame topicColorectal Cancer Screening and DetectionFrench-language works237,207