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Record W4388038237 · doi:10.1177/02676591231211502

The rare case of double valve surgery in a patient with factor VII deficiency

2023· article· en· W4388038237 on OpenAlexaff
Friederike I. Schoettler, Ali Fatehi Hassanabad, Michael Chiu, André Ferland, Corey Adams

Bibliographic record

VenuePerfusion · 2023
Typearticle
Languageen
FieldMedicine
TopicHemophilia Treatment and Research
Canadian institutionsLibin Cardiovascular Institute of AlbertaUniversity of Calgary
Fundersnot available
KeywordsMedicineFactor VIISurgeryCardiac surgeryRecombinant factor VIIaSevere bleedingIntensive care medicineInternal medicine

Abstract

fetched live from OpenAlex

Performing cardiac surgery on patients with bleeding diatheses poses significant challenges since these patients are at an increased risk for complications secondary to excessive bleeding. Despite its rarity, patients with factor VII (FVII) deficiency may require invasive procedures such as cardiac surgery. However, we lack guidelines on their pre-, peri-, and post-operative management. As FVII deficiency is rare, it seems unlikely to design and learn from large clinical studies. Instead, we need to base our clinical decision-making on single reported cases and registry data. Herein, we present the rare case of a patient with FVII deficiency who underwent double valve surgery. Pre-operatively, activated recombinant FVII (rFVIIa) was administered to reduce the risk of bleeding. Nevertheless, the patient experienced major bleeding. This case highlights the significance of FVII deficiency in patients undergoing cardiac surgery and emphasizes the importance of adequate and appropriate transfusion of blood products for these 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.

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)0.000
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.005
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.001
Science and technology studies0.0030.002
Scholarly communication0.0020.002
Open science0.0010.002
Research integrity0.0070.005
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 head0.045
GPT teacher head0.314
Teacher spread0.269 · 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 designCase report
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

Citations1
Published2023
Admission routes1
Has abstractyes

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