Clinical implications of decision making in colorectal polypectomy: an international survey of Western endoscopists suggests priorities for change
Bibliographic record
Abstract
Abstract Introduction Colonoscopy prevents colorectal cancer via the detection and resection of premalignant polyps. This effect may be attenuated by variations in polypectomy, with multiple techniques available and a wide range of experience amongst endoscopists. We assessed current practice against the best available contemporary evidence. Methods An online survey was distributed to members of the gastroenterological and surgical societies of seven countries during July 2017. Images of colorectal polyps were presented and respondents requested to provide the polypectomy technique they would employ in their daily practice. Responses were compared to the evidence-based techniques in the 2017 ESGE Colorectal Polypectomy Guideline. Results In total, 707 endoscopists (627 physicians, 71 surgeons, 9 nurse endoscopists, median practice duration 18 years) completed the survey. Of these, 3.1 % selected hot biopsy forceps and 5.2 % hot snare polypectomy (without submucosal lifting) to remove a 3 mm ascending colon polyp. Only 43.3 % selected cold snare polypectomy (CSP) to remove an 8 mm ascending colon polyp. Surgical referral was selected by 16.7 % of respondents for a 45 mm transverse colon polyp without endoscopic evidence of submucosal invasive cancer (SMIC). Endoscopic resection was selected by 12.0 % for an 80 mm sigmoid polyp with imaging consistent with deep SMIC, and a further 26.4 % selected tertiary endoscopist referral, suggesting they had not appreciated that it was endoscopically unresectable. Conclusion CSP is underutilized for small polyp resection despite its favorable safety and efficacy. Benign polyps are commonly referred for surgery and overt SMIC is underappreciated using endoscopic imaging. Addressing these issues may reduce diathermy-related adverse events, surgery, and unnecessary colonoscopic procedures for patients and reduce rates of post-colonoscopy colorectal cancer.
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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 | 0.010 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".