A narrative review of postoperative bleeding in patients with gastric cancer treated with endoscopic submucosal dissection
Bibliographic record
Abstract
Endoscopic submucosal dissection (ESD) is now considered a standard treatment for selected patients with early gastric cancer. Compared with endoscopic mucosal resection (EMR), ESD provides a higher complete resection rate (R0), and therefore, a lower local recurrence rate. However, ESD is a more time-consuming procedure, creating a wider and deeper ulcer floor which may cause complications. Post-ESD bleeding is one of them. Although most post-ESD bleedings can be controlled by endoscopic hemostasis at the time of operation, some bleeding after ESD may result in serious conditions such as hemorrhagic shock. Even with preventive methods such as ulcer closure, the application of fibrin glue and polyglycolic acid shielding, acid secretion inhibitors and hemostasis on second-look endoscopy, our experiences told us that post-ESD bleeding cannot be entirely avoidable, especially for patients with big size ulcer bed, anticoagulants/antithrombosis and chronic kidney diseases. The present review first defined post-ESD bleeding, then the incidence, the risk factors, such as the location of operative lesion, the size and depth, chronic kidney diseases, the impacts of anticoagulant and antithrombotic agents. We finally reviewed the managements of post-ESD bleeding, including approaches of coagulating potential bleeding spots during the procedure, lesion closure, lesion shielding and the application of gastric acid secretion inhibitors.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".