Endoscopic approach to large non-pedunculated colorectal polyps
Bibliographic record
Abstract
Large non-pedunculated colorectal polyps ≥20 mm (LNPCPs) constitute approximately 1% of all colorectal polyps and present a spectrum of risks, including overt and covert submucosal invasive cancer (T1 colorectal cancer (CRC)). Importantly, a curative resection may be achieved for LNPCPs with superficial T1 CRC (T1a or T1b <1000 µm into submucosa), if an enbloc R0 excision (clear margins) with favourable histology is achieved (ie, absence of high-grade tumour budding, lympho-vascular invasion, and poor differentiation). Thus, while consensus recommendations advocate for endoscopic resection as the primary treatment option for LNPCPs, thorough optical assessment is imperative for selecting the most suitable ER strategy. In this review, we highlight the critical components of optical evaluation that assist in predicting the risk of T1 CRC, including morphology (Paris and LST classifications), surface pit/vascular pattern (JNET and Kudo classifications), and lesion location. Different resection modalities, including endoscopic submucosal dissection and endoscopic mucosal resection are discussed, along with important considerations that may influence the resection strategy of choice, such as access to the LNPCP and submucosal fibrosis.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".