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Record W2005586329 · doi:10.1155/2015/194931

Awareness and Uptake of Family Screening in Patients Diagnosed with Colorectal Cancer at a Young Age

2015· article· en· W2005586329 on OpenAlexaboutno aff
Niamh M. Hogan, Marion Hanley, Aisling Hogan, O. J. McAnena, Mark P. Regan, Michael J. Kerin, Myles Joyce

Bibliographic record

VenueGastroenterology Research and Practice · 2015
Typearticle
Languageen
FieldMedicine
TopicColorectal Cancer Screening and Detection
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineFamily historyColorectal cancerDiseaseFirst-degree relativesPopulationCancerColorectal cancer screeningInternal medicineFamily medicinePediatricsColonoscopy

Abstract

fetched live from OpenAlex

Background. One-fifth of people who develop colorectal cancer (CRC) have a first-degree relative (FDR) also affected. There is a large disparity in guidelines for screening of relatives of patients with CRC. Herein we address awareness and uptake of family screening amongst patients diagnosed with CRC under age 60 and compare guidelines for screening. Study Design. Patients under age 60 who received surgical management for CRC between June 2009 and May 2012 were identified using pathology records and theatre logbooks. A telephone questionnaire was carried out to investigate family history and screening uptake among FDRs. Results. Of 317 patients surgically managed for CRC over the study period, 65 were under age 60 at diagnosis (8 deceased). The mean age was 51 (30-59). 66% had node positive disease. 25% had a family history of colorectal cancer in a FDR. While American and Canadian guidelines identified 100% of these patients as requiring screening, British guidelines advocated screening for only 40%. Of 324 FDRs, only 40.9% had been screened as a result of patient's diagnosis. Conclusions. Uptake of screening in FDRs of young patients with CRC is low. Increased education and uniformity of guidelines may improve screening uptake in this high-risk population.

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 score0.115
Threshold uncertainty score0.625

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.099
GPT teacher head0.386
Teacher spread0.286 · 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

Citations12
Published2015
Admission routes1
Has abstractyes

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