Establishment of Standards for the Referral of Large Non-Pedunculated Colorectal Polyps: An International Expert Consensus Using a Modified Delphi Process
Bibliographic record
Abstract
Aims Resection of colorectal polyps has been shown to decrease the incidence and mortality of colorectal cancer. Large non-pedunculated colorectal polyps are often referred to expert centres for endoscopic resection, which requires relevant information to be conveyed to the therapeutic endoscopist to allow for triage and planning of resection technique. Methods A Delphi methodology was employed to establish consensus on minimum expected standards for the referral of large colorectal polyps among a panel of international endoscopy experts. The expert panel was recruited through purposive sampling, and three rounds of surveys were conducted to achieve consensus, with quantitative and qualitative data analysed for each round. Results A total of 24 international experts from diverse continents participated in the Delphi study, resulting in consensus on 19 statements related to the referral of large colorectal polyps. The identified factors, including patient demographics, relevant medications, lesion factors, photodocumentation and the presence of a tattoo, were deemed important for conveying the necessary information to therapeutic endoscopists. The mean scores for the statements ranged from 7.04 to 9.29 out of 10, with high percentages of experts considering most statements as a very high priority. Subgroup analysis by continent revealed some variations in consensus rates among experts from different regions. Conclusions The identified consensus statements can aid in improving the triage and planning of resection techniques for large colorectal polyps, ultimately contributing to the reduction of colorectal cancer incidence and mortality. Publication History Article published online: 15 April 2024 © 2024. European Society of Gastrointestinal Endoscopy. All rights reserved. Georg Thieme Verlag KG Rüdigerstraße 14, 70469 Stuttgart, Germany
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.313 | 0.212 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".