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Record W3109036442 · doi:10.1182/hematology.2020000090

“TEG talk”: expanding clinical roles for thromboelastography and rotational thromboelastometry

2020· article· en· W3109036442 on OpenAlexaff
Rita Selby

Bibliographic record

VenueHematology · 2020
Typearticle
Languageen
FieldMedicine
TopicTrauma, Hemostasis, Coagulopathy, Resuscitation
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsThromboelastometryThromboelastographyMedicineCoagulationFibrinolysisThrombelastographyPlateletIntensive care medicineCoagulation testingCoagulopathySurgeryInternal medicine

Abstract

fetched live from OpenAlex

Viscoelastic assays (VEAs) that include thromboelastography and rotational thromboelastometry add value to the investigation of coagulopathies and goal-directed management of bleeding by providing a complete picture of clot formation, strength, and lysis in whole blood that includes the contribution of platelets, fibrinogen, and coagulation factors. Conventional coagulation assays have several limitations, such as their lack of correlation with bleeding and hypercoagulability; their inability to reflect the contribution of platelets, factor XIII, and plasmin during clot formation and lysis; and their slow turnaround times. VEA-guided transfusion algorithms may reduce allogeneic blood exposure during and after cardiac surgery and in the emergency management of trauma-induced coagulopathy and hemorrhage. However, the popularity of VEAs for other indications is driven largely by extrapolation of evidence from cardiac surgery, by the drawbacks of conventional coagulation assays, and by institution-specific preferences. Robust diagnostic studies validating and standardizing diagnostic cutoffs for VEA parameters and randomized trials comparing VEA-guided algorithms with standard care on clinical outcomes are urgently needed. Lack of such studies represents the biggest barrier to defining the role and impact of VEA in clinical care.

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)0.001
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 score0.045
Threshold uncertainty score0.729

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.071
GPT teacher head0.377
Teacher spread0.306 · 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

Citations49
Published2020
Admission routes1
Has abstractyes

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