A111 EVALUATION OF REFERRAL COMPLETENESS FOR LARGE NON-PEDUNCULATED POLYPS IN LIGHT OF RECENT INTERNATIONAL CONSENSUS: A SINGLE-CENTER STUDY
Bibliographic record
Abstract
Abstract Background Endoscopic mucosal resection (EMR) is standard-of-care for treating large non-pedunculated colorectal polyps yet often requires referral to expert centers. Hence, inclusion of important information to the therapeutic endoscopist is essential for pre-procedure planning. A recent international consensus statement, comprised of 19 components, aims to improve triage and planning of endoscopic resection for large non-pedunculated colorectal polyps. We sought to determine the current status of inclusion of these reporting elements in referrals to our tertiary center. Aims 1- Report the rate of complete referrals in light of the international expert consensus statement 2- Investigate the degree of correlation of polyp adjudication between referring endoscopist and local advance therapeutic endoscopiest assessment 3- Explore factors predicting comprehensive reporting Methods Single-center review of prospectively collected colorectal polyp referrals for large non-pedunculated polyps from March 2021 to March 2023. Results 411 referrals for large polyps were received; median size 3 cm; 58% located in ascending colon. 89 % of referrals included the initial assessment date, and only 38% incorporated video or photo documentation, of which 44% were deemed sufficient to demonstrate polyp features. Anatomical location was reported in 96% of referrals, while polyp size was mentioned in only half of the referrals (50%). Polyp morphology was described in 91% as either sessile (n=360) or pedunculated (n=35). Paris classification was reported in 53% of referrals, and LST classification in 90%. 12% of referrals reported four elements of less, while 18% of referrals reported all elements. Correlations with our own endoscopic assessments were diverse, ranging from a robust correlation for anatomical location (r=0.82, 95%CI 0.78-0.86, p < 0.001) to a more modest correlation for Paris classification (r=0.44, 95%CI 0.35-0.52 p < 0.001). Conclusions Our study reveals deficiencies in current referral practices and emphasizes the consensus statement’s role in improving the process. Consequences of low-quality referrals may include compromised patient care, leading to delayed diagnoses, inappropriate treatments, and potentially compromised patient outcomes. Thus, we have identified a likely knowledge gap in polyp characterization among referring physicians that will be a focus of a subsequent QI initiative. Funding Agencies None
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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 | 0.011 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".