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Record W4401697408 · doi:10.1001/jama.2024.11783

Tranexamic Acid in Patients Undergoing Liver Resection

2024· article· en· W4401697408 on OpenAlexaffabout
Paul J. Karanicolas, Yulia Lin, Stuart A. McCluskey, Jordan Tarshis, Kevin E. Thorpe, Alice C. Wei, Elijah Dixon, Geoff Porter, Prosanto Chaudhury, Sulaiman Nanji, Leyo Ruo, Melanie E. Tsang, Anton Skaro, Gareth Eeson, Sean P. Cleary, Carol-Anne Moulton, Chad G. Ball, Julie Hallet, Natalie Coburn, Pablo E. Serrano, Shiva Jayaraman, Calvin Law, Ved Tandan, Gonzalo Sapisochín, David Nagorney, Douglas Quan, Rory L. Smoot, Steven Gallinger, Peter Metrakos, Trevor Reichman, Diederick Jalink, Sean Bennett, Francis Sutherland, Edward Solano, Michele Molinari, Ephraim Tang, Susanne G. Warner, Oliver F. Bathe, Jeffrey Barkun, Michael L. Kendrick, Mark J. Truty, Rachel Roke, Grace Xu, Myriam Lafrenière‐Roula, Gordon Guyatt

Bibliographic record

VenueJAMA · 2024
Typearticle
Languageen
FieldMedicine
TopicBlood transfusion and management
Canadian institutionsImpactLondon Health Sciences CentreUniversity of British ColumbiaSt Joseph's Health CentreMcMaster UniversityJuravinski HospitalQueen's UniversityQueen Elizabeth II Health Sciences CentreFoothills Medical CentreKingston Health Sciences CentreMcGill University Health CentreMcGill UniversityUniversity of CalgaryDalhousie UniversityWestern UniversityUniversity of TorontoUniversity Health NetworkHealth Sciences CentrePublic Health OntarioKelowna General HospitalSunnybrook Health Science Centre
FundersNational Cancer Institute
KeywordsTranexamic acidMedicinePlaceboPerioperativeBlood transfusionRandomizationAnesthesiaAntifibrinolyticOdds ratioSurgeryRandomized controlled trialBlood lossInternal medicine

Abstract

fetched live from OpenAlex

Importance: Tranexamic acid reduces bleeding and blood transfusion in many types of surgery, but its effect in patients undergoing liver resection for a cancer-related indication remains unclear. Objective: To determine whether tranexamic acid reduces red blood cell transfusion within 7 days of liver resection. Design, Setting, and Participants: Multicenter randomized clinical trial of tranexamic acid vs placebo conducted from December 1, 2014, to November 8, 2022, at 10 hepatopancreaticobiliary sites in Canada and 1 site in the United States, with 90-day follow-up. Participants, clinicians, and data collectors were blinded to allocation. A volunteer sample of 1384 patients undergoing liver resection for a cancer-related indication met eligibility criteria and consented to randomization. Interventions: Tranexamic acid (1-g bolus followed by 1-g infusion over 8 hours; n = 619) or matching placebo (n = 626) beginning at induction of anesthesia. Main Outcomes and Measures: The primary outcome was receipt of red blood cell transfusion within 7 days of surgery. Results: The primary analysis included 1245 participants (mean age, 63.2 years; 39.8% female; 56.1% with a diagnosis of colorectal liver metastases). Perioperative characteristics were similar between groups. Red blood cell transfusion occurred in 16.3% of participants (n = 101) in the tranexamic acid group and 14.5% (n = 91) in the placebo group (odds ratio, 1.15 [95% CI, 0.84-1.56]; P = .38; absolute difference, 2% [95% CI, -2% to 6%]). Measured intraoperative blood loss (tranexamic acid, 817.3 mL; placebo, 836.7 mL; P = .75) and total estimated blood loss over 7 days (tranexamic acid, 1504.0 mL; placebo, 1551.2 mL; P = .38) were similar between groups. Participants receiving tranexamic acid experienced significantly more complications compared with placebo (odds ratio, 1.28 [95% CI, 1.02-1.60]; P = .03), with no significant difference in venous thromboembolism (odds ratio, 1.68 [95% CI, 0.95-3.07]; P = .08). Conclusions and Relevance: Among patients undergoing liver resection for a cancer-related indication, tranexamic acid did not reduce bleeding or blood transfusion but increased perioperative complications. Trial Registration: ClinicalTrials.gov Identifier: NCT02261415.

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)0.000
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.932
Threshold uncertainty score0.236

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.014
GPT teacher head0.247
Teacher spread0.233 · 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 designObservational
Domainnot available
GenreEmpirical

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

Citations35
Published2024
Admission routes2
Has abstractyes

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