The flushing method: a novel hemostatic technique for endoscopic submucosal dissection in insufflation and saline-immersion conditions
Bibliographic record
Abstract
Background and Aims: Saline-immersion endoscopic submucosal dissection (ESD) offers improved visualization. It may also provide better buoyancy for the tissue to facilitate the dissection. However, achieving reliable hemostasis remains technically challenging. This article presents the "flushing method," a novel hemostatic technique enabling consistent precoagulation and bleeding control under insufflation and immersion conditions. Methods: The flushing method was developed by connecting an endoscopic irrigation pump (EIP 2; Erbe Elektromedizin GmbH, Tübingen, Germany) to a FlushKnife BT-S (Fujifilm, Tokyo, Japan) via an extension tube. Pressing the foot pedal activates both the electrosurgical unit and the irrigation pump simultaneously, ejecting saline solution from the base of the knife. This can eliminate the vaporization layer generated by high-frequency coagulation while suppressing the electrical discharge. This allows stable and safe coagulation even at higher-power settings. Results: The technique was applied during ESD for a 150-mm circumferential rectal tumor in a 55-year-old woman. A total of 129 vessels (22 branching and 107 penetrating) were precoagulated using forcedCOAG (effect 5.5) with the irrigation pump flushing. Bleeding events (n = 14) were controlled with the knife alone, without hemostatic forceps. Conclusions: The flushing method is a safe and effective technique for precoagulation and hemostasis during ESD under both insufflation and saline-immersion conditions. Its versatility may broaden the clinical applicability of 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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".