Clinical outcomes of per‐oral endoscopic tumor resection for submucosal tumors in the esophagus and gastric cardia
Bibliographic record
Abstract
OBJECTIVES: The clinical success of per-oral endoscopic myotomy (POEM) has led to the development of a new field of 'submucosal endoscopy'. This study aimed to evaluate the safety, efficacy, and limitations of per-oral endoscopic tumor resection (POET) in the management of submucosal tumors (SMTs) in the esophagus and the gastric cardia. METHODS: POET was performed in 47 patients from January 2011 to December 2017. The indication for POET was SMTs ≤ 30 mm in minor axis diameter. Patient and tumor characteristics (age, gender, tumor location, size, and histology), operative and clinical results of POET (procedure time and completion rate, en bloc resection rate, length of hospitalization, adverse events and tumor recurrence) were analyzed retrospectively. RESULTS: POET was successfully completed in 43 patients (91.5%) without any major adverse events (Clavien-Dindo IIIb-IV). Four patients required conversion to an open surgical procedure due to suboptimal visualization during POET. Four patients underwent piecemeal resection of their SMTs including GISTs. Median follow-up was 44 months (10-96 months), during that time, there were no incidences of tumor recurrence. Tumors that had a minor axis diameter > 30 mm or a tumor mass index (TMI) [major axis diameter (mm) × minor axis diameter (mm)] >1000 had a high likelihood of being converted to surgical resection. CONCLUSIONS: POET is a safe and effective treatment for SMTs. However, in patients where the minor axis diameter is > 30 mm or the TMI > 1000, surgical excision should be considered. Furthermore, application of POET for SMTs with malignant potential should be carefully considered to ensure optimal oncologic outcomes.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.001 | 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".