Adrenaline Injection Plus Argon Plasma Coagulation versus Adrenaline Injection Plus Hemoclips for Treating High-Risk Bleeding Peptic Ulcers: A Prospective, Randomized Trial
Bibliographic record
Abstract
BACKGROUND/OBJECTIVE: Several combination endoscopic therapies are currently in use. The present study aimed to compare argon plasma coagulation (APC) + adrenaline injection (AI) with hemoclips + AI for the treatment of high-risk bleeding peptic ulcers. METHODS: In a prospective randomized trial, 172 patients with major stigmata of peptic ulcer bleeding were randomly assigned to receive APC + AI (n = 89) or hemoclips + AI (n = 83). In the event of rebleeding, the initial modality was used again. Patients in whom treatment or retreatment was unsuccessful underwent emergency surgery. The primary end point of rebleeding rate and secondary end points of initial and definitive hemostasis need for surgery and mortality were compared between the two groups. RESULTS: The two groups were similar in all background variables. Definitive hemostasis was achieved in 85 of 89 (95.5%) of the APC + AI and 82 of 83 (98.8%) of the hemoclips + AI group (P = 0.206). The mean volume of adrenaline injected in the two groups was equal (20.7 mL; P = 0.996). There was no significant difference in terms of initial hemostasis (96.6% versus 98.8%; P = 0.337), rate of rebleeding (11.2% versus 4.8%; P = 0.124), need for surgery (4.5% versus 1.2%; P = 0.266) and mortality (2.2% versus 1.2%; P = 0.526). When compared for the combined end point of mortality plus rebleeding and the need for surgery, there was an advantage for the hemoclip group (6% versus 15.7%, P = 0.042). CONCLUSION: Hemoclips + AI has no superiority over APC + AI in treating patients with high-risk bleeding peptic ulcers. Hemoclips + AI may be superior when a combination of all negative outcomes is considered.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".