Topical Hemostatic Agents for the Management of Malignancy-Related Gastrointestinal Bleeding: A Meta-Analysis
Bibliographic record
Abstract
Introduction: Malignancy-related gastrointestinal bleeding (MRGIB) is difficult to manage with conventional endoscopic techniques. The ability to treat large surface areas as well as the opportunity for non-traumatic application, make THAs ideal for the management of MRGIB. Although case reports and observational studies have demonstrated the potential of some THAs for the management of MRGIB, treatment efficacy may vary by the THA used, study design, study setting and outcomes measured. Here we present the results of a systematic review and meta-analysis examining the efficacy of THAs for achieving hemostasis in MRGIBs. Methods: This systematic review searched MEDLINE and EMBASE bibliographies of included articles and conference proceedings for studies reporting original data regarding the application of THAs for MRGIB. The pooled proportion of patients achieving and maintaining hemostasis for 72 hours or longer was calculated using random effects models. Heterogeneity was explored using stratified meta-analysis and meta-regression. Results: Of the 1,704 citations identified, a full-text review was performed on 65 and 11 were included in the meta-analysis (55 patients). Immediate hemostasis was 100%. Meta-analysis showed that treatment with THAs resulted in continued hemostasis at 72 hours or longer in 80% of cases (95% confidence interval 0.67-0.89), with no significant differences in the pooled estimate based on study size, study design, bleed location, treatment timing or type of THA used. Conclusion: THAs appear to be highly effective at achieving immediate hemostasis and maintaining hemostasis for at least 72-hours post treatment for MRGIB. Randomized control trials are needed to confirm the efficacy of THAs in this setting.Figure 1
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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 | 0.009 | 0.019 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.051 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".