Comparison of fibrin sealants in peripheral vascular surgery: A systematic review and network meta-analysis
Bibliographic record
Abstract
BACKGROUND: Evidence comparing fibrin sealants (FSs) in surgery are limited. This study evaluated the efficacy and safety of FSs, and manual compression in peripheral vascular surgery. METHODS: A systematic review of randomized trials was conducted in Medline, Embase, and Cochrane databases within the last 15 years. Data were available to conduct a network meta-analysis (NMA) in peripheral vascular surgery. Fibrin sealant treatment arms were further broken-down and assessed by clotting time (i.e., 2-min [2C] or 1-min [1C]). The primary efficacy outcome was the proportion of patients achieving hemostasis by 4 min (T4). Treatment-related serious and non-serious adverse events (AEs) were qualitatively assessed. RESULTS: Five studies (n = 693), were included in the NMA. Results predicted VISTASEAL 2C, followed by EVICEL 1C, had the highest probability of achieving T4. Compared with manual compression, significant improvements in T4 were found with VISTASEAL 2C (relative risk [RR] = 2.67, 95% CrI: 2.13-3.34), EVICEL 1C (RR = 2.58, 95% CrI: 2.04-3.23), VISTASEAL 1C (RR = 2.00, 95% CrI: 1.45-2.65), and TISSEEL 2C (RR = 1.99, 95% CrI: 1.48-2.60). TISSEEL 1C was not significantly different than manual compression (RR = 1.40, 95% CrI: 0.70-2.33). Among FSs, VISTASEAL 2C was associated with a significant improvements in T4 compared with VISTASEAL 1C (RR = 1.33, 95% CrI: 1.02-1.82), TISSEEL 2C (RR = 1.34, 95% CrI: 1.05-1.77), and TISSEEL 1C (RR = 1.90, 95% CrI: 1.18-3.74). Treatment-related serious and non-serious AE rates were typically lower than 2%. CONCLUSIONS: In peripheral vascular surgeries, VISTASEAL 2C and EVICEL 1C were shown to have the highest probabilities for achieving rapid hemostasis among the treatments compared. Future studies should expand networks across surgery types as data become available.
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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 | 0.016 | 0.030 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.020 | 0.035 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".