Endoscopic treatment of Barrett's esophagus: What can we learn from the Western perspective?
Bibliographic record
Abstract
The incidence of Barrett's esophagus (BE)-related neoplasia in Western countries has increased in the past several decades and, even in Eastern countries, it appears to be increasing. Endoscopic therapies are the first-line treatment for BE-related neoplasia; however, there is still no standardized treatment strategy. Most of the data have been published from Western countries where the ultimate goal of treatment is complete eradication of BE mucosa removing subtle synchronous lesions and preventing metachronous neoplasia. A multimodality approach that combines endoscopic resection and radiofrequency ablation (RFA) has been widely accepted in the West. In contrast, the lack of access to RFA treatment in the East has meant that endoscopic resection is the only feasible option. There is a wide divergence in treatment strategies for BE-related neoplasia between the East and the West. It is very important to consider these basic differences in the context of the currently available evidence to date. Therefore, the purpose of this article is to review the recent literature and to provide an overview of the endoscopic treatment options for BE.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".