876 Veriset Haemostatic Patch, Indications, Benefits and Complications: A Systematic Review
Bibliographic record
Abstract
Abstract Aim Achieving haemostasis intraoperatively is important for minimising blood loss, complications, and operation time. Suturing, cauterisation, fibrin glues and patches are used for this purpose. We explore Veriset, a patch consisting of polyethylene glycol and oxidised cellulose, to determine and compare its safety and effectiveness. Method Medline, Embase, Web of Science, Scopus, Cinahl and Cochrane databases were searched. Data points collected were Study/subject characteristics/demographics, surgery/specialty, time to haemostasis, proportion of haemostasis achieved, intraoperative adverse events, post operative complications, follow up time, and biochemical/histological analysis. Risk of Bias was assessed by Newcastle-Ottawa Scale Results Six studies were included; four human trials (3 RCTs, 1 case series) and two animal trials. The human trials combined had 250 patients, with 147 using Veriset. In two RCTs, Veriset showed faster time to haemostasis and higher proportion of haemostasis achieved vs suturing and Tachosil haemostatic patch. In all three RCTs, no significant differences in adverse events and complications were seen between Veriset and suturing/Tachosil. Vascular, nephrectomy and hepatic surgery were investigated. In the animal studies, the pig trial showed similar effectiveness and safety as the human trials. The rat study compared novel experimental patches to Veriset, and showed similar effectiveness to Veriset, at reduced costs. Conclusions Although current literature is scarce, Veriset is more effective than alternatives for haemostasis, with similar safety, although there are now other experimental patches that could have better health economic implications. Further clinical trials would be necessary to determine the breadth of applicable surgical fields for Veriset.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".