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Record W4213201186 · doi:10.1093/jcag/gwab049.105

A106 THE EPIDEMIOLOGY OF COMPLEX COLONIC POLYPS: A POPULATION BASED STUDY OF THE SOUTHWEST ONTARIO COLONOSCOPY COHORT

2022· article· en· W4213201186 on OpenAlexaffabout
A Almudaires, Shareef Alqahtani, Victoria Siebring, Cassandra McDonald, S Lee, Michael Sey, Brian Yan

Bibliographic record

VenueJournal of the Canadian Association of Gastroenterology · 2022
Typearticle
Languageen
FieldMedicine
TopicGastric Cancer Management and Outcomes
Canadian institutionsLawson Health Research InstituteOttawa Regional Cancer FoundationWestern University
Fundersnot available
KeywordsColonoscopyMedicineEpidemiologyCohortPopulationLogistic regressionCohort studyInternal medicineProspective cohort studyMultivariate analysisSurgeryColorectal cancerCancerEnvironmental health

Abstract

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Abstract Background Complex polyps are well recognized amongst endoscopists, but its definition varies in the literature and from one endoscopist to another. Despite its clinical importance, the epidemiology of complex polyps is poorly understood. Aims To assess the epidemiology of complex polyps on a population level, and in FIT positive individuals. Methods The Southwest Ontario Colonoscopy cohort is a prospective database consisting of all adult patients undergoing colonoscopy at 21 hospitals in Southwest Ontario. Data is collected through a mandatory quality assurance form completed by the endoscopist after each procedure. All outpatient adult colonoscopies for any indication were included. Incomplete colonoscopies, repeat procedures, and poor preparation colonoscopies were excluded. A manual review of the colonoscopy report was completed in cases where the description of the complex polyps was missing. The primary outcomes were the prevalence of complex polyps in the cohort, and in FIT positive patients. Secondary outcomes include endoscopic description of the complex polyp, rates of attempted and complete resection, and identification of possible associations between patient and endoscopist factors with complex polyp detection and removal. A multivariate logistic regression model was generated to assess for factors associated with complex polyp detection. Results From February 2019 to December 2020, 43389 colonoscopies were included, of which 1459 were for FIT positive patients. 2294 patients had a complex polyp, with a prevalence of 5.3% [95% CI 0.051–0.055], while the prevalence was 17.1% [95% CI 0.152–0.191] in the FIT positive cohort. Compared to average-risk patients undergoing colonoscopy for colon cancer screening, the odds ratio (OR) of detecting a complex polyp in individuals with positive FIT was 4.12 [95% CI 3.42–4.98, p<0.0001]. Among complex polyps,1324 (57.7%) were described as large (>2cm) and 1290 (56%) described as sessile. Of 2294 patients with complex polyps,1992 (86.8%) [95% CI 0.855–0.882] underwent a removal attempt, with successful complete removal as determined by the endoscopist achieved in 1905 patients (95.6%) [95% CI 0.947–0.965]. Compared to gastroenterologists, general surgeons and internists were less likely to detect a complex polyp, OR 0.67 [95% CI 0.61–0.73, p <0.0001] and 0.36 [95% CI 0.20–0.67, p=0.0011] respectively. Trainee involvement was associated with higher rate of complex polyp detection, OR 1.20 [95% CI 1.07–1.35, p=0.0022]. Females were less likely to have a complex polyp compared to males, OR 0.71 [95% CI 0.65–0.77, p<0.0001]. Conclusions Complex polyps are more prevalent than previously reported in the literature, with a high prevalence among the FIT positive population compared to other indications of colonoscopy. Funding Agencies None

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)0.001
metaresearch head score (Gemma)0.002
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 score0.257
Threshold uncertainty score0.516

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
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)0.0020.000

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 head0.026
GPT teacher head0.271
Teacher spread0.246 · 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".

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Citations0
Published2022
Admission routes2
Has abstractyes

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