Cardiac Murmurs in The Newborn – When to Worry?
Bibliographic record
Abstract
Abstract Congenital Heart Disease (CHD) contributes to a large proportion of mortality among infants and young children. Newborns (birth to 1 month of age) are at higher risk of having a serious lesion requiring early intervention, than older infants and children. Detecting a murmur in a newborn on physical exam can provide a clue to the presence of CHD, but its utility is limited by provider expertise and neonatal factors, such as rapid heart rate and respiratory symptoms. Furthermore, not all murmurs are pathological. Health care providers including primary care physicians, pediatricians or nurse practitioners often face difficulties when determining whether a murmur warrants further investigation. We aim to describe key differences between innocent and pathological heart murmurs in the newborn. Further, we describe current screening protocols for Critical Congenital Heart Defects (CCHD) that may assist primary care physicians in deciding when to refer for further evaluation. Keywords Congenital Heart Disease, Murmur, Pulse Oximetry Screening Abbreviations AAP: American Academy of Pediatrics; AS: Aortic Stenosis; AVSD: Atrioventricular Septal Defects; CCHD: Critical Congenital Heart Defects; CHD: Congenital Heart Disease; CPS: Canadian Paediatric Society; PDA: Patent Ductus Arteriosus; PFO: Patent Foramen Ovale; POS: Pulse Oximetry Screening; TGA: Transposition of The Great Arteries; VSD: Ventricular Septal Defect
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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 | 0.001 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".