Nonauscultatory clinical criteria are sensitive for cardiac pathology in low-risk paediatric heart murmurs
Bibliographic record
Abstract
BACKGROUND: Healthy children with likely innocent heart murmurs are frequently referred to cardiologists for reassurance. Existing guidelines that advise against these referrals are not consistently followed partly because they involve subjective auscultatory judgements with which many care providers are uncomfortable. Here, we investigate whether clinical criteria with no subjective auscultatory component are sensitive for cardiac pathology. METHODS: A retrospective chart review was performed of all new patients seen in our paediatric cardiology clinic for assessment of a murmur from January 1, 2016 through June 30, 2018. Patients were characterized as "low-risk" if they met all of the following criteria: asymptomatic; normal physical examination other than the murmur; no risk factors for congenital heart disease; and age over 12 months. The primary outcomes were the sensitivity for ruling out pathology and the negative predictive value of the proposed criteria. RESULTS: Of 915 total patients, 214 met the low-risk criteria. The sensitivity of our criteria for ruling out pathology was 97.2% (95% confidence interval 94.1% to 99.0%) and the negative predictive value was also 97.2% (95% confidence interval 94.0% to 98.7%). Six of the 214 low-risk patients had pathology (2.8%; 95% confidence interval 1.3% to 6.0%), none of which has required intervention since diagnosis. Each of these six children had a murmur that sounded pathological to the auscultating cardiologist. CONCLUSIONS: Basic clinical criteria that do not require auscultation are highly sensitive for ruling out significant cardiac pathology in children over 12 months of age.
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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 | 0.004 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".