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Record W4391882696 · doi:10.1080/00325481.2024.2320080

Plasma: indications, controversies, and opportunities

2024· article· en· W4391882696 on OpenAlexaff
Michael A. Benson, Deborah Tolich, Jeannie Callum, Moisés Aurón

Bibliographic record

VenuePostgraduate Medicine · 2024
Typearticle
Languageen
FieldMedicine
TopicTrauma, Hemostasis, Coagulopathy, Resuscitation
Canadian institutionsQueen's University
Fundersnot available
KeywordsMedicineFresh frozen plasmaCoagulopathyDosingIntensive care medicineWarfarinCoagulationTransfusion medicineCoagulation testingBlood productBlood transfusionSurgeryPharmacologyInternal medicine

Abstract

fetched live from OpenAlex

Plasma is overused as a blood product worldwide; however, data supporting appropriate use of plasma is scant. Its most common utilization is for treatment of coagulopathy in actively bleeding patients; it is also used for coagulation optimization prior to procedures with specific coagulation profile targets. A baseline literature review in PUBMED and Google Scholar was done (1 January 2000 to 1 June 2023), utilizing the following search terms: plasma, fresh frozen plasma, lyophilized plasma, indications, massive transfusion protocol, liver disease, warfarin reversal, cardiothoracic surgery, INR < 2. An initial review of the titles and abstracts excluded all articles that were not focused on transfusional medicine. Additional references were obtained from citations within the retrieved articles. This narrative review discusses the main indications for appropriate plasma use, mainly coagulation factor replacement, major hemorrhage protocol, coagulopathy in liver disease, bleeding in the setting of vitamin K antagonists, among others. The correlation between concentration of coagulation factors and INR, as well as the proper plasma dosing with its volume being weight-based, is also discussed. A high value approach to plasma utilization is supported with a review of the clinical situations where plasma is overutilized or unnecessary. Finally, a discussion of novel plasma products is presented for enhanced awareness.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.783
Threshold uncertainty score0.676

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.068
GPT teacher head0.313
Teacher spread0.245 · 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 designNot applicable
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

Citations3
Published2024
Admission routes1
Has abstractyes

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