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Effect of perioperative norepinephrine infusion on the outcomes after microsurgical free tissue grafting: a systematic review

2025· review· en· W4415430145 on OpenAlexaboutno aff
M.S. Filimonov, Nino V. Abdiba, Yulia A. Fedorova, L. A. Rodomanova

Bibliographic record

VenueRussian Journal of Anesthesiology and Reanimatology · 2025
Typereview
Languageen
FieldMedicine
TopicAnesthesia and Pain Management
Canadian institutionsnot available
Fundersnot available
KeywordsPerioperativeNorepinephrineRandomized controlled trialOdds ratioIncidence (geometry)TransplantationClinical trial

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.185
Threshold uncertainty score0.908

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0070.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.010
GPT teacher head0.309
Teacher spread0.299 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
Domainnot available
GenreReview

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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