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Record W2804703429 · doi:10.1139/cjpp-2018-0002

Patient-specific multiscale computational fluid dynamics assessment of embolization rates in the hybrid Norwood: effects of size and placement of the reverse Blalock–Taussig shunt

2018· article· en· W2804703429 on OpenAlexvenueno aff
Ray Prather, John Seligson, Marcus Ni, Eduardo Divo, Alain J. Kassab, William M. DeCampli

Bibliographic record

VenueCanadian Journal of Physiology and Pharmacology · 2018
Typearticle
Languageen
FieldMedicine
TopicAortic Disease and Treatment Approaches
Canadian institutionsnot available
Fundersnot available
KeywordsEmbolizationNorwood procedureMedicineShunt (medical)ArchSurgeryAortic archHypoplastic left heart syndromeCardiologyAortaHeart disease

Abstract

fetched live from OpenAlex

The hybrid Norwood operation is performed to treat hypoplastic left heart syndrome. Distal arch obstruction may compromise flow to the brain. In a variant of this procedure, a synthetic graft (reverse Blalock-Taussig shunt) is placed between the pulmonary trunk and innominate artery to improve upper torso blood flow. Thrombi originating in the graft may embolize to the brain. In this study, we used computational fluid dynamics and particle tracking to investigate the patterns of particle embolization as a function of the anatomic position of the reverse Blalock-Taussig shunt. The degree of distal arch obstruction and position of particle origin influence embolization probabilities to the cerebral arteries. Cerebral embolization probabilities can be reduced by as much as 20% by optimizing graft position, for a given arch geometry, degree of distal arch obstruction, and particle origin. There is a tradeoff, however, between cerebral pulmonary and coronary embolization probabilities.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.009
GPT teacher head0.275
Teacher spread0.266 · 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 designSimulation or modeling
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

Citations5
Published2018
Admission routes1
Has abstractyes

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Same venueCanadian Journal of Physiology and PharmacologySame topicAortic Disease and Treatment ApproachesFrench-language works237,207