MétaCan
Menu
Back to cohort
Record W4414410880 · doi:10.3390/ebj6030052

Effectiveness and Safety of Topically Applied Tranexamic Acid with Epinephrine in Surgical Procedures: A Systematic Review

2025· review· en· W4414410880 on OpenAlexaff
Hedieh Keshavarz, Weber Wei Chiang Lin, Shawn Dodd, Janice Y. Kung, Joshua N. Wong

Bibliographic record

VenueEuropean Burn Journal · 2025
Typereview
Languageen
FieldMedicine
TopicBlood transfusion and management
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsTranexamic acidEpinephrinePerioperativeDosingClinical trial

Abstract

fetched live from OpenAlex

BACKGROUND: Topical tranexamic acid (TXA), often combined with epinephrine, is used to reduce perioperative bleeding. This systematic review evaluates the safety and effectiveness of this combination across surgical procedures. METHODS: A comprehensive search of eight databases was conducted from inception to 26 June 2025. Studies were eligible if they compared topically or locally applied TXA with epinephrine to epinephrine alone in surgical patients. Animal studies, case reports, non-English publications, and studies without comparators were excluded. Screening, data extraction, and risk of bias assessments followed PRISMA guidelines. RESULTS: Ten studies met inclusion criteria (four randomized and six non-randomized), covering burn surgery, rhytidectomy, liposuction, septoplasty, endoscopic sinus surgery, dacryocystorhinostomy, and joint arthroplasty. TXA was applied topically or via tumescent infiltration. Most studies reported reduced intraoperative blood loss, improved surgical field visibility, lower drain output, shorter hemostasis time, and reduced transfusion rates. No increase in thromboembolic or major complications was observed. CONCLUSION: The combination of TXA and epinephrine appears safe and maybe effective for perioperative bleeding control. However, heterogeneity in dosing and outcomes limits generalizability. Further research is needed to standardize protocols and confirm long-term safety.

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)0.000
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 score0.109
Threshold uncertainty score0.864

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.000
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 head0.012
GPT teacher head0.278
Teacher spread0.265 · 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

Citations1
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueEuropean Burn JournalSame topicBlood transfusion and managementFrench-language works237,207