A126 ASSESSING THE QUALITY OF REFERRALS AND ADJUDICATION FOR ENDOSCOPIC RESECTION OF LARGE COLORECTAL POLYPS AT A CANADIAN TERTIARY REFERRAL CENTRE
Bibliographic record
Abstract
Abstract Background Management of large colorectal polyps is increasingly complex with the expansion of endoscopic techniques, including endoscopic submucosal dissection (ESD), endoscopic mucosal resection (EMR), and endoscopic full thickness resection. Adjudicating lesions in an effort to select the optimal resection method is hugely dependent on the information included within the referral. At our institution, a referral pathway based on photo and/or video documentation was created to facilitate the timely assessment and treatment of large colorectal polyps. To date, little is known about quality of referrals for endoscopic resection of large colorectal polyps. Purpose Our study aimed to assess the adjudication process and quality of referrals for endoscopic resection of large colorectal polyps at our advanced endoscopy referral center. Method We conducted a single-center prospective study of consecutive colorectal polyps referred for EMR from March 2021 to March 2022. Cases selected upfront for ESD were excluded. Referral information, intraprocedural data and histology was captured. No procedural and histology data were captured if EMR does not occur after adjudication. The outcome was defined as the frequency of adequate referrals. A referral was deemed adequate if it contained: sufficient photo or video documentation, description of any characteristics that increase the difficulty of endoscopic resection, accurate polyp localization/size estimate (with <1 cm discrepancy when compared to real-time endoscopic evaluation), and description of any endoscopic features of advanced dysplasia (AD), including HGD/IMCa, or submucosal invasion (SMI). Result(s) During the study period, 213 referrals were received for colorectal polyps and underwent adjudication for EMR: 211 underwent EMR; 2 underwent ESD despite being triaged for EMR. Only 5% (10/213) of referrals were deemed to be adequate. Only 34% (73/213) contained any photo or video documentation and only 13% (28/213) photos/videos were of sufficient quality for adjudication. Difficult location or polyp characteristics, if present, were accurately described in 86.7% of referrals (183/211) of referrals. The accurate polyp location was described 80.6% of the time (170/211). Polyp size was estimated in 50.2% (107/213) of referrals. Amongst referrals with size estimated, the size was accurate in 73.8% of the time (79/107). On histological evaluation, 35.1% (74/211) of polyps had AD or SMI. Amongst polyps with AD or SMI, 48.6% (36/74) had endoscopic appearance suggestive of HGD/IMCa/SMI but only 69.4% (25/36) of these polyps with high-risk endoscopic features were accurately predicted based on the referral information. Conclusion(s) Referrals for large colorectal polyps often lack important clinical information. This significantly impairs the ability to adjudicate polyps for triage and resection and may negatively impact patient outcomes. To improve referral adequacy and patient outcomes, we plan to evaluate the impact of polyp adjudication on EMR success. Please acknowledge all funding agencies by checking the applicable boxes below None Disclosure of Interest None Declared
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".