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The evidence for the use of recombinant factor VIIa in massive bleeding: development of a transfusion policy framework

2008· article· en· W4238609292 on OpenAlexaffabout
Catherine Moltzan, David R. Anderson, Jeannie Callum, Stephen E. Fremes, Heather Hume, C. David Mazer, M.‐C. Poon, Georges‐Étienne Rivard, Sandro Rizoli, Sheria G. Robinson‐Lane

Bibliographic record

VenueTransfusion Medicine · 2008
Typearticle
Languageen
FieldMedicine
TopicHemophilia Treatment and Research
Canadian institutionsCanadian Blood ServicesCancerCare ManitobaHealth Sciences CentreSt. Boniface HospitalSunnybrook Health Science CentreSt. Michael's Hospital
Fundersnot available
KeywordsRecombinant factor VIIaHaemophiliaMedicineFactor VIIaRandomized controlled trialIntensive care medicineClotting factorCoagulationBlood transfusionSurgeryInternal medicineTissue factor

Abstract

fetched live from OpenAlex

A review of the recent randomized control trial evidence of the use of recombinant factor VIIa (rFVIIa) in massive bleeding. rFVIIa is a recombinant genetically engineered clotting factor that has been used for the management of haemophilia patients with inhibitors. There has been increasing use in patients with massive bleeding, even when there is no underlying coagulation disorder present. In November 2006, the Canadian National Advisory Committee on Blood and Blood Products engaged in a consultation and review process with several leading Canadian experts to review and discuss the current evidence up to November 2006. There is little evidence to support the routine use of rFVIIa in massive bleeding on review of 13 randomized controlled trials. rFVIIa should only be considered as part of a transfusion policy framework for massive bleeding after all other transfusion and supportive measures are considered. An example of a policy framework is presented.

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)0.142
metaresearch head score (Gemma)0.272
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.142
Threshold uncertainty score0.753

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1420.272
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0070.006
Bibliometrics0.0090.009
Science and technology studies0.0010.005
Scholarly communication0.0070.007
Open science0.0040.002
Research integrity0.0070.007
Insufficient payload (model declined to judge)0.0030.001

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 head0.265
GPT teacher head0.406
Teacher spread0.140 · 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 designTheoretical or conceptual
Domainnot available
GenreMethods

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

Citations21
Published2008
Admission routes2
Has abstractyes

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