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Record W4297611767 · doi:10.1017/9781316536001.013

Perioperative Monitoring for Congenital Heart Disease Surgery

2022· book-chapter· en· W4297611767 on OpenAlexaff
Shavonne L. Massey, Robert M. Clancy

Bibliographic record

VenueCambridge University Press eBooks · 2022
Typebook-chapter
Languageen
FieldNeuroscience
TopicAnesthesia and Neurotoxicity Research
Canadian institutionsSickKids FoundationHospital for Sick ChildrenUniversity of Toronto
Fundersnot available
KeywordsMedicineHeart diseaseCardiac surgerySequelaPerioperativeCardiopulmonary bypassPopulationPediatricsDiseaseHypoplastic left heart syndromeHypothermiaIntensive care medicineIncidence (geometry)SurgeryCardiologyAnesthesiaInternal medicine

Abstract

fetched live from OpenAlex

Congenital heart disease (CHD) encompasses a large collection of cardiac malformations discovered at or before birth. CHD has an incidence of 4 – 50/1000 live births annually. One quarter of these require surgery shortly after birth. Newborn heart surgery has substantially changed since the modern era began with the adaptation of adult cardiopulmonary bypass (CPB) circuitry for infants. After decades of progress, the center of focus has now shifted from survival to the quality of life following newborn heart surgery (NBHS). Indeed, neurodevelopmental disabilities are now considered the single most common sequela of NBHS. Clinical management in the peri-operative period has a significant impact on the infants’ long-term outcomes. Consequently, neurological monitoring in the congenital heart disease population is increasing worldwide. With so many infants undergoing NBHS, the field of neuromonitoring for these patients is wide. In this chapter, we first review the neurological effects of hypothermia and the actual conduct of newborn heart surgery. We then discuss the indications for neuromonitoring and summarize its findings and outcomes in this unique population.

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)0.000
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: Other · Consensus signal: Other
Teacher disagreement score0.962
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.085
GPT teacher head0.261
Teacher spread0.177 · 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
GenreOther

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
Published2022
Admission routes1
Has abstractyes

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