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Record W2760976407 · doi:10.1213/ane.0000000000002504

Patient Blood Management in Pediatric Cardiac Surgery: A Review

2017· review· en· W2760976407 on OpenAlexaff
Jill M. Cholette, David Faraoni, Susan M. Goobie, Victor A. Ferraris, Nabil Hassan

Bibliographic record

VenueAnesthesia & Analgesia · 2017
Typereview
Languageen
FieldMedicine
TopicBlood transfusion and management
Canadian institutionsSickKids FoundationHospital for Sick ChildrenUniversity of Toronto
Fundersnot available
KeywordsMedicineBlood managementBlood productIntensive care medicineCardiopulmonary bypassPerioperativeCardiac surgeryCoagulopathyAnemiaPopulationBlood conservationHypothermiaBlood transfusionAnesthesiaSurgeryInternal medicine

Abstract

fetched live from OpenAlex

Efforts to reduce blood product transfusions and adopt blood conservation strategies for infants and children undergoing cardiac surgical procedures are ongoing. Children typically receive red blood cell and coagulant blood products perioperatively for many reasons, including developmental alterations of their hemostatic system, and hemodilution and hypothermia with cardiopulmonary bypass that incites inflammation and coagulopathy and requires systemic anticoagulation. The complexity of their surgical procedures, complex cardiopulmonary interactions, and risk for inadequate oxygen delivery and postoperative bleeding further contribute to blood product utilization in this vulnerable population. Despite these challenges, safe conservative blood management practices spanning the pre-, intra-, and postoperative periods are being developed and are associated with reduced blood product transfusions. This review summarizes the available evidence regarding anemia management and blood transfusion practices in the perioperative care of these critically ill children. The evidence suggests that adoption of a comprehensive blood management approach decreases blood transfusions, but the impact on clinical outcomes is less well studied and represents an area that deserves further investigation.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.881
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0050.002
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.051
GPT teacher head0.323
Teacher spread0.272 · 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.

Study designNot applicable
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

Citations84
Published2017
Admission routes1
Has abstractyes

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