Polyp Resection - Controversial Practices and Unanswered Questions
Bibliographic record
Abstract
Detection and complete removal of precancerous neoplastic polyps are central to effective colorectal cancer screening. The prevalence of neoplastic polyps in the screening population in the United States is likely >50%. However, most persons with neoplastic polyps are never destined to develop cancer, and do not benefit for finding and removing polyps, and may only be harmed by the procedure. Further 70-80% of polyps are diminutive (≤5 mm) and such polyps almost never contain cancer. Given the questionable benefit, the high-cost and the potential risk changing our approach to the management of diminutive polyps is currently debated. Deemphasizing diminutive polyps and shifting our efforts to detection and complete removal of larger and higher-risk polyps deserves discussion and study. This article explores three controversies, and emerging concepts related to endoscopic polyp resection. First, we discuss challenges of optical resect-and-discard strategy and possible alternatives. Second, we review recent studies that support the use of cold snare resection for ≥5 mm polyps. Thirdly, we examine current evidence for prophylactic clipping after resection of large polyps.
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.
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 | 0.030 | 0.084 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.012 |
| Scholarly communication | 0.004 | 0.009 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".