Using newborn screening analytes to identify cases of neonatal sepsis
Bibliographic record
Abstract
Neonatal sepsis is associated with high mortality and morbidity, yet challenges with available diagnostic approaches can lead to delays in therapy. Our study assessed whether newborn screening analytes could be utilized to identify associations with neonatal sepsis. We linked a newborn screening registry with health databases to identify cases of sepsis among infants born in Ontario from 2010-2015. Correlations between sepsis and screening analytes were examined within three gestational age groups (early preterm: <34 weeks; late preterm: 34-36 weeks; term: ≥37 weeks), using multivariable logistic regression models. We started with a model containing only clinical factors, then added groups of screening analytes. Among 793,128 infants, 4,794 were diagnosed with sepsis during the neonatal period. Clinical variables alone or in combination with hemoglobin values were not strongly predictive of neonatal sepsis among infants born at term or late preterm. However, model fit improved considerably after adding markers of thyroid and adrenal function, acyl-carnitines, and amino acids. Among infants born at early preterm gestation, neither clinical variables alone nor models incorporating screening analytes adequately predicted neonatal sepsis. The combination of clinical variables and newborn screening analytes may have utility in identifying term or late preterm infants at risk for neonatal sepsis.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".