The use of Cap-Mounted Clips as primary hemostatic modality in Nonvariceal Upper Gastrointestinal Bleeding: Current role and future perspectives
Bibliographic record
Abstract
Nonvariceal upper gastrointestinal bleeding (NVUGIB) is an important condition that continues to exert a significant burden on healthcare systems. Despite improvement in the medical and endoscopic therapies for NVUGIB, the morbidity and mortality of this condition remain unchanged, largely related to the increasing age and comorbidities of the affected population. Several endoscopic modalities are available to manage bleeding lesions, but a significant proportion of patients suffer from primary failure in achieving hemostasis or rebleed after initial successful hemostasis, which carry worsened outcomes for patients. Recently, the management of NVUGIB has seen significant evolution with the introduction of several novel endoscopic tools including cap-mounted clips. These clips have been utilized in managing patients with NVUGIB as a primary or rescue therapy with promising results. Several randomized controlled studies have been published in recent years addressing the role of such clips yielding overall favorable outcomes. In this article, we will review the recent updates in the role of cap-mounted clips in the management of NVUGIB, while identifying current limitations in the evidence that are pertinent to the adoption of this hemostatic modality by clinicians in routine practice.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".