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Record W2321921599 · doi:10.1177/2150135112460866

Bleeding and Thrombotic Emergencies in Pediatric Cardiac Intensive Care

2012· article· en· W2321921599 on OpenAlexaff
Therese M. Giglia, James A. DiNardo, Nancy S. Ghanayem, Rebecca Ichord, Robert A. Niebler, Kirsten C. Odegard, M. Patricia Massicotte, Andrew R. Yates, Peter C. Laussen, James S. Tweddell

Bibliographic record

VenueWorld Journal for Pediatric and Congenital Heart Surgery · 2012
Typearticle
Languageen
FieldEngineering
TopicMechanical Circulatory Support Devices
Canadian institutionsStollery Children's HospitalUniversity of Alberta
Fundersnot available
KeywordsMedicineIntensive care medicineIntensivistThrombosisIntensive care unitInternal medicine

Abstract

fetched live from OpenAlex

Children in the cardiac intensive care unit (CICU) with congenital or acquired heart disease are at risk for hematologic complications, both hemorrhage and thrombosis. The overall incidence of hematologic complications in the CICU is unknown, but risk factors and target groups have been identified where the essential physiologic balance between bleeding and clotting has been disrupted. Although the best management of life-threatening bleeding and clotting is prevention, the cardiac intensivist is often faced with managing life-threatening hematologic events involving patients from within the unit or those who present from outside. Part I of this review deals with the propensity of children with congenital and acquired heart disease to complications of both bleeding and clotting, and includes discussions of perioperative bleeding, thromboses in single-ventricle patients, clotting of Blalock-Taussig shunts and thrombotic complications of mechanical valves. Part II deals with the subject of stroke in children with heart disease. Part III reviews monitoring the effectiveness of anticoagulation and thrombolysis in the CICU. Currently available diagnostics modalities, medications and management strategies are reviewed and future directions discussed.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.111
Threshold uncertainty score0.882

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.026
GPT teacher head0.248
Teacher spread0.222 · 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 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

Citations15
Published2012
Admission routes1
Has abstractyes

Explore more

Same venueWorld Journal for Pediatric and Congenital Heart SurgerySame topicMechanical Circulatory Support DevicesFrench-language works237,207