Endoscopic Submucosal Dissection for Superficial Esophageal Neoplasms
Bibliographic record
Abstract
Background and Aim: There has been a marked increase in the number of esophageal squamous cell neoplasms (SCNs) which are applicable to local treatment by virtue of recent developments in endoscopy. Endoscopic submucosal dissection (ESD) is an effective treatment for early noninvasive gastrointestinal cancers. Aim of this study was to evaluate efficacy of ESD for superficial esophageal squamous cell neoplasms. Subjects & Methods: Between November 2010 and March 2012, seventy-two lesions with superficial esophageal squamous cell neoplasms, which were treated by ESD in Kumamoto University Hospital, were analyzed in this study. Therapeutic efficacy, complications, and follow-up results were assessed. Results: Mean size of the lesions was 19±14 mm (range; 1-80 mm); and mean size of the resection specimens was 29±13 mm (range; 7- 80mm). Extensive lesions over 2/3 of the circumference were observed in 17 patients. En bloc resection rate was 100% (72/72), and en block resection rate with tumor-free lateral/basal margins was 95.8% (69/72). Perforation didn’t occur. Endoscopic dilation was performed for post-operative stenosis in 10 patients. In 3 patients who developed pinhole-like stenosis followed by circumferential ESD, combination treatment of oral steroid administration with endoscopic dilation could achieve favorable courses. None of the patients developed local recurrence or distant metastasis in the follow-up period. Conclusion: ESD is a minimally invasive, relatively safe treatment method for esophageal SCNs. In particular, it is suggested that ESD combined with oral steroid administration and endoscopic dilation, might be applicable to the patients with circumferential SCNs where post-operative stenosis must follow circumferential ESD.
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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 | 0.000 |
| 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".