Characteristics of Patients with Colonic Polyps Requiring Segmental Resection
Bibliographic record
Abstract
Background. It is unclear if the availability of new techniques for removal of large colonic polyps has affected the use of segmental colon resection. We sought to evaluate the characteristics of polyps undergoing surgical resection, including involvement of therapeutic gastroenterologists (TG). Methods. 484 patients had a colonic resection; 165 (34%) were identified from the pathology database with polyp, adenoma, or mass in the clinical history field; these charts were reviewed. Results. 128 patients (mean age 68 yrs, 72% male) were included. The mean polyp size was 2.9 cm (0.4 cm–12.0 cm). Adenocarcinoma was diagnosed in 50 (39.1%). 97 (75.8%) patients had a polyp that was felt to be unresectable by EMR, and 31 (24.2%) underwent successful EMR followed by surgery for adenocarcinoma ( n=29 ). The indication for surgery in those with unresectable polyps was variable and was not clearly documented in 51 (52.6%); only 17 of these patients (17.5%) had a TG involved. Conclusion. A high proportion of polyps managed by segmental resection did not contain adenocarcinoma. This data suggests that even in a tertiary care center where advanced endoscopic techniques are easily available, they are not always utilized. Educational endeavors to ensure that ideal pathways of intervention are utilized require implementation.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.003 | 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".