A Meta-Analysis of Using Protamine for Reducing the Risk of Hemorrhage During Carotid Recanalization: Direct Comparisons of Post-operative Complications
Bibliographic record
Abstract
Background: Protamine can decrease the risk of hemorrhage during carotid recanalization. However, it may cause severe side effects. There is no consensus on the safety and efficacy of protamine during surgery. Thus, we conduct a comprehensive review and meta-analysis to compare the differences between the protamine and the no-protamine group. Method: We systematically obtained literature from Medline, Google Scholar, Cochrane Library, and PubMed electronic databases. All four databases were scanned from 1937 when protamine was first adopted as a heparin antagonist until February 2021. The reference lists of identified studies were manually checked to determine other eligible studies that qualify. The articles were included in this meta-analysis as long as they met the criteria of PICOS; conference or commentary articles, letters, case report or series, and animal observation were excluded from this study. The Newcastle-Ottawa Quality Assessment Scale and Cochrane Collaboration’s tool are used to assess the risk of bias of each included observational study and RCT, respectively. Stata version 12.0 statistical software (StataCorp LP, College Station, Texas) was adopted as statistical software. When I2 < 50%, we consider that the data have no obvious heterogeneity, and we conduct a meta-analysis using the fixed-effect model. Otherwise, the random-effect model was performed. Result: A total of 11 studies, consisting of 94,618 participants, are included in this study. Our analysis found that the rate of wound hematoma had a significant difference among protamine and no-protamine patients (OR = 0.268, 95% CI = 0.093 to 0.774, p = 0.015). Furthermore, the incidence of hematoma requiring re-operation (0.7%) was significantly lower than that of patients without protamine (1.8%). However, there was no significant difference in the incidence of stroke, wound hematoma with hypertension, transient ischemic attacks (TIA), myocardial infarction (MI), and death. Conclusion: Among included participants undergoing recanalization, the use of protamine is effective in reducing hematoma without increasing the risk of having other complications. Besides, more evidence-based performance is needed to supplement this opinion due to inherent limitations.
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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 | 0.022 | 0.046 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.082 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| 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".