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Factors in the Initial Resuscitation of Patients With Severe Trauma

2025· article· en· W4414383598 on OpenAlexafffundabout
Luís Teodoro da Luz, Keyvan Karkouti, Jo Carroll, Deep Grewal, Yulia Lin, Akash Gupta, Avery B. Nathens, Amie Kron, Lowyl Notario, Andrew Beckett, Andrew Petrosoniak, Katerina Pavenski, Kelly Vogt, Ian Ball, Neil Parry, Paul T. Engels, Michelle P. Zeller, Donald M. Arnold, Emilie P. Belley‐Côté, Chris Evans, Jordan Leitch, Andrew W. Shih, Philip Dawe, R. K. Gooch, Jeannie Callum

Bibliographic record

VenueJAMA Network Open · 2025
Typearticle
Languageen
FieldMedicine
TopicTrauma, Hemostasis, Coagulopathy, Resuscitation
Canadian institutionsUniversity of British ColumbiaVancouver General HospitalKingston General HospitalQueen's UniversityHamilton Health SciencesUniversity of TorontoWestern UniversityPublic Health OntarioToronto Public HealthSunnybrook Health Science CentreSinai Health SystemUniversity Health NetworkHealth Sciences CentreLondon Health Sciences CentreHamilton General HospitalWomen's College HospitalMcMaster UniversityToronto General Hospital
FundersTakeda CanadaCanadian Blood ServicesCSL BehringCanadian Institutes of Health ResearchAbbott Laboratories
KeywordsResuscitationSevere traumaSevere sepsisShock (circulatory)Disease

Abstract

fetched live from OpenAlex

Importance: Patients with bleeding and coagulopathic trauma often require more transfusions and have higher mortality rates, motivating research on improving hemostatic strategies. Objective: To evaluate the replacement of clotting factors with frozen plasma (FP) or factor concentrates (fibrinogen concentrate [FC] and prothrombin complex concentrate [PCC]) in the initial resuscitation of patients with trauma. Design, Setting, and Participants: This multicenter, parallel-control, superiority randomized clinical trial was conducted at 6 level I trauma centers in Canada, between April 2021 and February 2023. Eligible patients were those with massive hemorrhage protocol (MHP) activation on admission and aged 16 years or older. Patients were excluded if they received more than 2 red blood cell (RBC) units either before hospital admission or in the hospital prior to randomization or if they had a catastrophic head injury. Follow-up was completed on March 25, 2023. The primary analysis was based on the modified intention-to-treat approach. Interventions: The intervention group received FC 4 g and PCC 2000 IU in MHP packs 1 and 2. The control group received 4 FP units. Concurrently, patients received 4 RBC units (both packs) and 1 dose of platelets (pack 2). After pack 2, FP was administered at clinician discretion. Main Outcomes and Measures: Primary outcome was the number of allogeneic blood product (RBC, FP, and platelet) units administered within 24 hours. Secondary outcomes included incidence of thromboembolic events, duration of intensive care unit stay, and mortality. Results: Of the 217 patients enrolled, 107 were randomly assigned to the FC-PCC group and 110 to the FP group; 137 patients were included in the primary analysis (66 in the FC-PCC group and 71 in the FP group). Baseline characteristics were similar between groups (median [IQR] age, 38 [29-55] years; 111 males [81.0%]). Among these patients, 95 (69.3%) had blunt mechanisms of injury, and the median (IQR) Injury Severity Score was 29 (19-43). Mean 24-hour transfusions were 20.8 (95% CI, 16.7-25.9) units in the FC-PCC group and 23.8 (95% CI, 19.2-29.4) units in the FP group. The mean ratio was 0.87 (1-sided 97.5% CI, 0.00-1.19; P = .20 for superiority). No significant differences were found in thromboembolic complications or 24-hour and 28-day mortality. The trial was terminated after the interim analysis showed conditional power of less than 25%, requiring an impractically large sample to show superiority. Conclusions and Relevance: In this randomized clinical trial, clotting factor concentrates were not superior to FP for initial resuscitation of patients with trauma. Efficacy and safety outcomes were similar across the treatment groups. Trial Registration: ClinicalTrials.gov Identifier: NCT04534751.

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)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.050
Threshold uncertainty score0.371

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.040
GPT teacher head0.324
Teacher spread0.283 · 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

Citations6
Published2025
Admission routes3
Has abstractyes

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