Endoscopic resection and histological evaluation of colorectal polyps: Is it a definitive treatment?
Bibliographic record
Abstract
BACKGROUND AND AIMS: Primary aim of the present study was the evaluation of efficacy and safety of endoscopic polypectomy in a tertiary advanced endoscopic laboratory in Northwestern Greece. Additional aim was to estimate the effectiveness of endoscopic treatment of colorectal polyps and record the clinical course. METHODS: One hundred and fifty consecutive patients (97 men) with colorectal polyps of size larger than 0.5 cm were included. The size, topography, shape and presence of pedicle were recorded for every polyp. Concerning the size, polyps were divided into: <1 cm, between 1-2 cm, >2 cm. RESULTS: The rectum and sigmoid were the most common sites of detection (76.6%). Endoscopic resection was successful and the complication rate was very low (2.6%). The majority of the removed polyps were neoplastic (87.1%). Most neoplastic polyps were tubulovillous adenomas (50.8%). Low-grade dysplasia was detected in most of the polyps (82.9%), but highgrade dysplasia or invasive carcinoma was also detected in some patients. In total, 10 patients underwent surgical resection. Regular follow-up did not reveal significant residual polyps or recurrence of the lesions. CONCLUSION: Endoscopic polypectomy is effective and safe and leads to complete resection of neoplastic polyps in the majority of cases.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".