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Record W3095961332 · doi:10.1097/shk.0000000000001685

Back to the Future: Whole Blood Resuscitation of the Severely Injured Trauma Patient

2020· article· en· W3095961332 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueShock · 2020
Typearticle
Languageen
FieldMedicine
TopicTrauma, Hemostasis, Coagulopathy, Resuscitation
Canadian institutionsUniversity of CalgaryCanadian Armed ForcesOttawa HospitalFoothills Medical CentreUniversity of Ottawa
Fundersnot available
KeywordsWhole bloodResuscitationBlood componentBlood transfusionBlood typingTrauma centerBlood productBlood type (non-human)

Abstract

fetched live from OpenAlex

ABSTRACT: Following advances in blood typing and storage, whole blood transfusion became available for the treatment of casualties during World War I. While substantially utilized during World War II and the Korean War, whole blood transfusion declined during the Vietnam War as civilian centers transitioned to blood component therapies. Little evidence supported this shift, and recent conflicts in Iraq and Afghanistan have renewed interest in military and civilian applications of whole blood transfusion. Within the past two decades, civilian trauma centers have begun to study transfusion protocols based upon cold-stored, low anti-A/B titer type O whole blood for the treatment of severely injured civilian trauma patients. Early data suggests equivalent or improved resuscitation and hemostatic markers with whole blood transfusion when compared to balanced blood component therapy. Additional studies are taking place to define the optimal way to utilize low-titer type O whole blood in both prehospital and trauma center resuscitation of bleeding patients.

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.

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 score0.676
Threshold uncertainty score0.381

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.019
GPT teacher head0.249
Teacher spread0.230 · 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