Hybrid Laparoendoscopic Approaches to Endoscopically Unresectable Colon Polyps
Bibliographic record
Abstract
Secondary prevention of colorectal cancer relies on effective screening through colonoscopy and polypectomy. Resection of some polyps can present technical challenges particularly when polyps are large, flat, or behind colonic folds. Laparoscopy as an adjunct to endoscopy can aid in removing difficult colonic polyps without subjecting patients to radical segmental colectomy. Hybrid laparoendoscopic techniques are increasingly reported in literature as alternatives to segmental colectomy for the treatment of polyps that have a high likelihood of being benign. Laparoscopic-assisted colonoscopic polypectomy is the most frequently utilized technique; it harnesses the power of laparoscopy to aid endoscopic polypectomy by flattening folds, mobilizing flexures, and providing retraction. Colonoscopy-assisted laparoscopic wedge and transluminal resection are often reported in older studies and use the visualization provided by intraoperative colonoscopy to guide colonic resection that is limited to the area of the polyp. Laparoscopic-assisted endoscopic full-thickness resection (EFTR) is a relatively recent technique that provides laparoscopic monitoring of EFTR of polyp as well as endoscopic closure of the ensuing defect. Minimally invasive segmental colectomy based on oncologic principles should be utilized when none of the previous techniques are suitable or when malignancy is strongly suspected. The combined use of laparoscopy and endoscopy can expand the endoscopist's armamentarium when dealing with the most challenging polyps, while serving the patients' best interest by limiting the extent of colon resection.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".