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Record W2966252625

USE OF ROTATIONAL THROMBOELASTOMETRY FOR OPTIMAL MANAGEMENT OF EARLY RESUSCITATION OF BLEEDING TRAUMA PATIENTS

2009· dissertation· en· W2966252625 on OpenAlexaboutno aff
Precilla Veigas

Bibliographic record

VenueTSpace · 2009
Typedissertation
Languageen
FieldArts and Humanities
TopicHermeneutics and Narrative Identity
Canadian institutionsnot available
Fundersnot available
KeywordsThromboelastometryResuscitationMedicineAnesthesiaSurgeryCoagulopathy
DOInot available

Abstract

fetched live from OpenAlex

Early trauma-induced coagulopathy (TIC) is associated with uncontrollable bleeding, high transfusion requirements and mortality. Current management strategies of coagulopathic bleeding trauma patients are either guided by untimely laboratory tests (goal-directed) or blind transfusion protocols according to pre-established formulas (damage-control, 1:1:1). Both strategies have significant limitations that may account for the poor outcome of many patients. Viscoelastometric tests such as rotational thromboelastometry (ROTEM) are emerging technologies with potential to revolutionize resuscitation of rapidly bleeding trauma patients. This project aims to investigate the role of ROTEM in early trauma resuscitation, including TIC diagnosis, guiding blood transfusion and predicting 24h mortality. The overarching hypothesis is that ROTEM parameters measured immediately upon hospital admission, may predict increased transfusion requirements, including massive transfusion (MT), and 24h mortality, and thus could guide blood and blood product transfusions. In order to explore this hypothesis, a series of approaches have been performed. A systematic review and international consensus conference with a panel of renowned experts in trauma, critical care, hematology, and surgery were done. They established the ROTEM parameters and values used for the diagnosis of TIC, guidance of blood transfusion and prediction of 24h mortality. Next, patient data from two major Canadian trauma centers were obtained to develop and validate prediction models for assessing the risk of requiring blood and blood product transfusion (red blood cells (RBC), plasma, cryoprecipitate and platelets), massive transfusion (MT) and 24h mortality using ROTEM and readily available clinical variables. The systematic review and the consensus conference indicated that low clot amplitude was associated with transfusion requirements, MT and 24h mortality but exact ROTEM parameters values could not be determined due to equipment limitations (i.e. high coefficient of variability) and poor understanding of their physiologic meaning (i.e. meaning of hyper coagulability parameters). Similarly, patient data analysis demonstrated that low clot amplitude, both in extrinsic (EXTEM) and fibrinogen pathways (FIBTEM), along with clinical parameters, accurately predicted the need for plasma and cryoprecipitate transfusion, MT, and 24h mortality. ROTEM however, did not predict RBC or platelet transfusion. The results of our studies indicate that ROTEM may assist in the early diagnosis and guide timely management of bleeding coagulopathic injured patients. While our findings warrant future evaluations we speculate that ROTEM can guide blood transfusions, reduce inappropriate blood utilization and improve patient outcome (reduce mortality).

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.015
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.011
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.015
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.063
GPT teacher head0.327
Teacher spread0.264 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2009
Admission routes1
Has abstractyes

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