Current standards and new developments of colorectal polyp management and resection techniques
Bibliographic record
Abstract
INTRODUCTION: Colonoscopy and endoscopic removal of precancerous polyps play an important role in colorectal cancer (CRC) prevention. Improved endoscopes and quality standards have led to an increasing polyp and adenoma detection rate. Optimal polyp resection techniques and management strategies are key for an effective colonoscopy practice. Areas covered: Strategies for how to improve diminutive polyp (polyps up to 5 mm in size) management are discussed because of their high prevalence. Systematic removal of diminutive polyps leads to increasing costs of colonoscopy practice, while the effect on colorectal cancer prevention might be negligible. Furthermore, polypectomy recommendations for mid-size and large polyps are provided. For all larger polyps larger, complete and safe resection is mandatory to avoid post colonoscopy cancers. The focus for managing such larger polyps is to use new techniques (i.e. cold snares) and to attempt complete removal and to reduce post-polypectomy complications. Expert commentary: The resect-and-discard strategy is a promising management strategy for diminutive polyps. However, modification of this approach might be required in order to make widespread adoption feasible. Cold snare polypectomy is a promising new approach for small polyp resection. For resection of large polyps adequate treatment recommendations with regard to endoscopic mucosal resection and complication prevention are provided.
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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".