Need for adjunctive removal techniques for endoscopic mucosal resection of large non-pedunculated colonic polyps is predictive of recurrence
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Background and study aims Endoscopic mucosal resection (EMR) allows for safe and effective removal of large non-pedunculated colon polyps, but recurrence remains a significant concern. Risk factors for recurrence have previously been reported, however, the significance of these factors have varied and has uncertain applicability with recent advances in EMR techniques. We aimed to evaluate rates and risk factors for recurrence in recent years from a major Canadian referral center. Patients and methods Consecutive patients between April 1, 2017 and March 1, 2019 who underwent piecemeal EMR were retrospectively identified. Patients with non-pedunculated colorectal polyps ≥ 2 cm removed by piecemeal EMR with available follow-up data were included. Results Five hundred and seventeen patients were reviewed, with 265 patients satisfying inclusion criteria. The median age was 67 years (IQR 14); 48 % were female. 15 % had a recurrence on follow-up endoscopy. Adjunctive removal techniques were utilized in 31 % of patients, 95 % of which was hot avulsion. The use of adjunctive removal techniques (OR 2.87, P = 0.004) and male gender (OR 3.31, P = 0.003) was significantly predictive of recurrence on multivariate analysis. Receiver operating curve characteristics demonstrated good performance of these factors in predicting recurrence (area under the curve = 0.70). Conclusions The use of adjunctive removal techniques, particularly hot avulsion and male gender are predictive of recurrence after piecemeal EMR of large non-pedunculated colorectal polyps. Male patients and those who require hot avulsion may be considered high risk for recurrence and warrant closer follow-up.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it