Hemostatic powders for gastrointestinal bleeding: a review of old, new, and emerging agents in a rapidly advancing field
Bibliographic record
Abstract
Abstract Background and study aims Hemostatic powders are increasingly used to address limitations in conventional endoscopic techniques for gastrointestinal bleeding. Various agents exist with different compositions, characteristics, efficacy, and adverse events (AEs). We sought to review existing hemostatic powders, from preclinical to established agents. Methods A literature review on hemostatic powders for gastrointestinal bleeding was undertaken through a MEDLINE search from 2000–2021 and hand searching of articles. Relevant literature was critically appraised and reviewed for mechanism of action, hemostasis and rebleeding rate, factors associated with hemostatic failure, and AEs. Results The most established agents are TC-325 (Hemospray), EndoClot, and Ankaferd Blood Stopper (ABS). These agents have been successfully applied to a variety of upper and lower gastrointestinal bleeding etiologies, in the form of primary, combination, salvage, and bridging therapy. Few AEs have been reported, including visceral perforation, venous embolism, and self-limited abdominal pain. Newer agents include CEGP-003 and UI-EWD, which have shown results similar to those for the older agents in initial clinical studies. All aforementioned powders have high immediate hemostasis rates, particularly in scenarios not amenable to conventional endoscopic methods, but are limited by significant rates of rebleeding. Other treatments include TDM-621 (PuraStat) consisting of a liquid hemostatic agent newly applied to endoscopy and self-propelling thrombin powder (CounterFlow Powder), a preclinical but promising agent. Conclusions Rapid development of hemostatic powders and growing clinical expertise has established these agents as a valuable strategy in gastrointestinal bleeding. Further research will continue to refine the efficacy and applicability of these agents.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".