Perioperative Monitoring for Congenital Heart Disease Surgery
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.009 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".