Issues and Prospects of Current Endoscopic Treatment Strategy for Superficial Non-Ampullary Duodenal Epithelial Tumors
Bibliographic record
Abstract
An increasing number of duodenal tumors are being diagnosed over the years, leading to increased confusion regarding the choice of treatment options. Small-to-large tumors and histological types vary from adenoma to carcinoma, and treatment methods may need to be selected according to lesion characteristics. Because of its anatomic characteristics, complications are more likely to occur in the duodenum than in other gastrointestinal organs. Several reports have described the outcomes of conventional endoscopic mucosal resection, endoscopic submucosal dissection, cold snare polypectomy, underwater endoscopic mucosal resection, endoscopic full-thickness resection, and laparoscopic and endoscopic cooperative surgery for duodenal tumors. However, even in the guidelines set out by various countries, only the treatment methods are listed, and no clear treatment strategies are provided. Although there are few reports with a sufficiently high level of evidence, considering the currently available treatment options is essential. In this report, we reviewed previous reports on each treatment strategy, discussed the current issues and prospects, and proposed the best possible treatment strategy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| 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".