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Record W4213362741 · doi:10.1093/jcag/gwab049.106

A107 WHAT SIZE CUT-OFF LEVEL SHOULD BE USED TO IMPLEMENT OPTICAL POLYP DIAGNOSIS?

2022· article· en· W4213362741 on OpenAlexaff
Mahsa Taghiakbari, Heiko Pohl, Roupen Djinbachian, J M Anderson, D Metellus, Alan Barkun, Mickaël Bouin, Daniel von Renteln

Bibliographic record

VenueJournal of the Canadian Association of Gastroenterology · 2022
Typearticle
Languageen
FieldMedicine
TopicBreast Lesions and Carcinomas
Canadian institutionsMontreal General HospitalMcGill UniversityCentre Hospitalier de l’Université de Montréal
Fundersnot available
KeywordsMedicineConfidence intervalInternal medicineGastroenterologyProspective cohort studyColonoscopyCancerColorectal cancer

Abstract

fetched live from OpenAlex

Abstract Background The risk of advanced pathology and potential mismanagement increases with polyp size while performing optical diagnosis. We hypothesized that a lower polyp size cut-off (e.g., 1–3 mm) would be associated with a lower risk of misclassifying advanced neoplasia or even cancer when using optical diagnosis. Aims This study aimed to evaluate the proportion of patients undergoing inadequate surveillance intervals associated with different size cut-offs when using optical diagnosis. Methods In a post-hoc analysis of three prospective studies, the use of optical diagnosis was evaluated for three polyp size groups: 1–3, 1–5, and 1–10 mm. The primary outcome was the proportion of patients in which advanced adenomas were found and optical diagnosis resulted in delayed surveillance in each group. Secondary outcomes included agreements between surveillance intervals based on high-confidence optical diagnosis and pathology outcomes, reduction in histopathological examinations, and proportion of patients who could receive an immediate surveillance recommendation. Results We included 3374 patients (7291 polyps ≤10 mm) undergoing complete colonoscopies (median age 66.0 years, 75.2% male, 29.6% for screening). Among polyp sized 1–3 mm, 1–5 mm, and 1–10 mm, 0.5%, 0.6%, and 1.2% of polyps, respectively, were found to have advanced pathology ( P <.0001). The percentage of patients with advanced adenomas and either 2- or 7- year delayed surveillance intervals (n=79) was 3.8%, 15.2%, and 25.3% for size cut-offs of 1–3, 1–5, and 1–10 mm polyps, respectively ( P<.0001). Surveillance interval agreements between pathology and high-confidence optical diagnosis for the three groups were 97.2%, 95.5%, and 94.2%, respectively. In the cohort of patients in which patients with normal colonoscopy, polyps >10 mm, and poor bowel preparation were excluded, the surveillance interval agreements between pathology and high-confidence optical diagnosis for the three groups were 96.2%, 93.6%, and 92.1%, respectively. Total reduction in pathology examinations for the three groups were 33.5%, 62.3%, and 78.2%, respectively. Furthermore, optical diagnosis could have allowed 41.0%, 58.2%, and 73.3% of patients, respectively, to be given immediate surveillance interval recommendations. Conclusions This study showed that limiting optical diagnosis to polyps 1–3 mm resulted in an excellent safety profile with a very low risk for inappropriate management of advanced adenomas, which makes routine clinical implementation of the “resect and discard” strategy feasible. Implementing a 3 mm cut-off could be a starting point for endoscopists to feel comfortable with the “resect and discard” strategy, with the potential of implementing a 5 mm cut-off, once optical diagnosis becomes more popular, and endoscopists become more comfortable with its use. Funding Agencies NoneNA

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)0.009
metaresearch head score (Gemma)0.047
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 score0.009
Threshold uncertainty score0.047

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.047
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.049
GPT teacher head0.289
Teacher spread0.240 · 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 routes1
Has abstractyes

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