High-Sensitivity Cardiac Troponin and the Management of Congenital Heart Disease in Newborns and Infants
Bibliographic record
Abstract
BACKGROUND: Early cardiac interventions in newborns and infants suspected for congenital heart disease (CHD) decrease morbidity and mortality. After updating current evidence on the use of cardiac troponins (cTn) in the context of CHD for risk stratification at early ages, we discuss relevant issues, starting from the evidence that only the measurement of the cTnT form is useful in this population. CONTENT: In newborns/infants with CHD, the cTnT concentration increase is correlated with: (a) cardiac stress and hemodynamic parameters, but not with the type of CHD; (b) volume overload/right ventricular pressure overload; (c) postoperative hypoperfusion injury and mortality; and (d) effects of cardioprotective strategies. For infants with CHD, high-sensitivity cTnT (hs-cTnT) concentrations >25 ng/L are an independent predictor of poor outcomes. Transitioning from cTnT to hs-cTnT in newborns/infants improves the identification of: (a) physiopathological mechanisms and factors that increased hs-cTnT early after birth; (b) myocardial injury, even when subclinical; (c) identification of patients requiring immediate therapeutic interventions; and (d) 99th percentile upper reference limits (URLs). However, no reliable URLs are currently available to allow the detection of myocardial injury associated with CHD in newborns/infants. SUMMARY: Additional data evaluating the clinical value of hs-cTnT in the risk stratification of newborns/infants with CHD who may suffer myocardial injury is needed. Validating the measurement, possibly in amniotic fluid samples, and improving the interpretation of hs-cTnT concentrations in the prenatal period, at birth and within 1 year of age are crucial to change CHD mortality/morbidity trends in the pediatric population.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".