Comparison between Endoscopic Submucosal Dissection and Transanal Endoscopic Microsurgery in Early Rectal Neuroendocrine Tumor Patients: A Meta-Analysis
Bibliographic record
Abstract
PURPOSE: To compare the effectiveness, safety and cost-effectiveness of endoscopic submucosal dissection (ESD) with transanal endoscopic microsurgery (TEM) in early rectal neuroendocrine tumor (RNET) patients. This article will provide reliable evidence for surgeons in regards to clinical decision-making. METHODS: Systematic literature retrieval was performed in Pubmed, Embase and Cochrane database from 2013/4/30 to 2023/4/30. Methodology validation was performed by using the Newcastle-Ottawa Scale (NOS). Data-analysis was conducted by using the Review manager version 5.3 software. RESULTS: A total of three retrospective studies were included in our meta-analysis. All eligible studies were considered to be high quality. By comparing baseline characteristics between TEM and ESD, patients in the TEM group seemed to be characterized by a larger tumor size and lower tumor level, even though no statistical significance was found. Clear statistical significance favoring TEM was identified in terms of R0 resection rate, procedure time and hospital stay. No statistical significance was found in terms of recurrence rate, adverse events rate and additional treatment rate. CONCLUSIONS: Compared with ESD, TEM was a more effective treatment modality for early RNET patients; it was associated with a relatively higher R0 resection rate and a similar degree of safety. However, the relatively higher cost and complicated manipulation restricted the promotion of TEM. Surgeons should opt for TEM as a primary treatment in patients with a larger tumor size and deeper degree of tumorous infiltration if the financial condition and hospital facility permit.
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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 | 0.010 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.045 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".