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Record W1967461352 · doi:10.1097/aco.0b013e32831a40a3

Efficacy and safety of activated recombinant factor VII in cardiac surgical patients

2009· review· en· W1967461352 on OpenAlexaff
Jean‐François Hardy, Sylvain Bélisle, P. Van der Linden

Bibliographic record

VenueCurrent Opinion in Anaesthesiology · 2009
Typereview
Languageen
FieldMedicine
TopicHemophilia Treatment and Research
Canadian institutionsHôpital Notre-DameUniversité de Montréal
Fundersnot available
KeywordsMedicineHemostasisRecombinant factor VIIaAdverse effectHemostatic AgentIntensive care medicineFactor VIIaCardiac surgeryRandomized controlled trialSurgeryMajor bleedingHemostaticsPatient safetyCoagulationInternal medicineMyocardial infarctionTissue factor

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: Excessive bleeding is a common and morbid problem after cardiac surgery. There is no doubt a need for an effective and safe hemostatic agent in order to minimize transfusions and avoid surgical reintervention for hemostasis. Recombinant activated factor VII (rFVIIa) is being used (off-label) increasingly after cardiac surgery to prevent or to control hemorrhage, but its efficacy and safety remain unclear. RECENT FINDINGS: Several case reports, case series and registries would tend to support the use of activated recombinant factor VII to control excessive bleeding after cardiac operations. On the contrary, two randomized controlled trials have produced negative results whereas a third has not been published yet. Adverse thrombotic events are reported with increasing frequency. SUMMARY: At present, the generalized use of rFVIIa to prevent or to control excessive bleeding after cardiac surgery cannot be recommended. The decision to administer a potent hemostatic such as rFVIIa outside its recognized prescribing indications should be made with caution by well informed physicians and discussed with the patient. Patients should be informed about knowledge gaps and pertinent risks, which are both important in the case of rFVIIa.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.975
Threshold uncertainty score0.987

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.111
GPT teacher head0.420
Teacher spread0.309 · 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 designOther design
Domainnot available
GenreReview

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

Citations20
Published2009
Admission routes1
Has abstractyes

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