Effect of perioperative norepinephrine infusion on the outcomes after microsurgical free tissue grafting: a systematic review
Bibliographic record
Abstract
The problem of maintaining target blood pressure in perioperative period after microsurgical interventions systematically requires catecholamine support (vasopressors). This leads to disputes about safety of norepinephrine after free tissue grafting. Objective. To determine the effect of perioperative norepinephrine infusion on the risk and incidence of postoperative complications after transplantation of free tissue complexes. Material and methods. We reviewed the eLibrary, PubMed (MEDLINE), Ovid, ScienceDirect, Google Scholar and Web of Science databases. When analyzing the risks of systematic bias, we used the Newcastle-Ottawa questionnaire for non-randomized clinical trials and the validated Cochrane RCT Assessment System questionnaire for randomized clinical trials. The meta-analysis was performed using the Open Meta-Analyst software. Results. The incidence of postoperative complications following perioperative norepinephrine infusion was 6%. A similar value was obtained in patients who did not receive vasopressor support. The pooled odds ratio was 0.62 (95% CI 0.22—1.79). The indicator was characterized by low heterogeneity (I2=0%, p=0.923). Conclusion. There are no significant differences in the incidence of postoperative complications and redo interventions after transplantation of free tissue complexes followed by perioperative norepinephrine infusion.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.009 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".