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Record W2092240508 · doi:10.1177/1089253209338076

Lessons Learned in Antifibrinolytic Therapy: The BART Trial

2009· article· en· W2092240508 on OpenAlexaff
John M. Murkin

Bibliographic record

VenueSeminars in Cardiothoracic and Vascular Anesthesia · 2009
Typearticle
Languageen
FieldMedicine
TopicBlood transfusion and management
Canadian institutionsLondon Health Sciences CentreWestern University
Fundersnot available
KeywordsMedicineAntifibrinolyticIntensive care medicineTranexamic acidSurgery

Abstract

fetched live from OpenAlex

Despite nearly 2 decades of published reports and clinical trials demonstrating the relative safety and efficacy of aprotinin in adult cardiac surgical patients at increased risk of bleeding-culminating in an official endorsement of the usage of aprotinin in such patients from both cardiac surgery and anesthesiology subspecialty committees-several more recent studies have raised profound concerns regarding the safety of aprotinin in these same patients. These studies and the implications thereof have ultimately resulted in the withdrawal of aprotinin from clinical usage internationally. This article will briefly review these developments with the hope of understanding how this abrupt turnabout took place and will attempt to understand how such events can be avoided in the future.

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)0.015
metaresearch head score (Gemma)0.025
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.015
Threshold uncertainty score0.077

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.025
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0030.005
Open science0.0010.001
Research integrity0.0040.006
Insufficient payload (model declined to judge)0.0040.001

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 head0.039
GPT teacher head0.331
Teacher spread0.292 · 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 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

Citations23
Published2009
Admission routes1
Has abstractyes

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