Identifying Genetic Susceptibility in Neonates With Hypoxic-Ischemic Encephalopathy: A Retrospective Case Series
Bibliographic record
Abstract
Neonatal hypoxic-ischemic encephalopathy is a clinical phenomenon that often results from perinatal asphyxia. To mitigate secondary neurologic injury, prompt initial assessment and diagnosis is needed to identify patients eligible for therapeutic hypothermia. However, occasionally neonates present with a clinical picture of hypoxic-ischemic encephalopathy without significant risk factors for perinatal asphyxia. We hypothesized that in patients with genetic abnormalities, the clinical manifestation of those abnormalities may overlap with hypoxic-ischemic encephalopathy criteria, potentially contributing to a causal misattribution. We reviewed 210 charts of infants meeting local protocol criteria for moderate to severe hypoxic-ischemic encephalopathy in neonatal intensive care units in Calgary, Alberta. All patients that met criteria for therapeutic hypothermia were eligible for the study. Data were collected surrounding pregnancy and birth histories, as well as any available genetic or metabolic testing including microarray, gene panels, whole-exome sequencing, and newborn metabolic screens. Twenty-eight patients had genetic testing such as microarray, whole-exome sequencing, or a gene panel, because of clinical suspicion. Ten of 28 patients had genetic mutations, including CDKL5, pyruvate dehydrogenase, CFTR, CYP21A2, ISY1, KIF1A, KCNQ2, SCN9A, MTFMT, and NPHP1. All patients lacked significant risk factors to support a moderate to severe hypoxic-ischemic encephalopathy diagnosis. Treatment was changed in 2 patients because of confirmed genetic etiology. This study demonstrates the importance of identifying genetic comorbidities as potential contributors to a hypoxic-ischemic encephalopathy phenotype in neonates. Early identification of clinical factors that support an alternate diagnosis should be considered when the patient's clinical picture is not typical of hypoxic-ischemic encephalopathy and could aid in both treatment decisions and outcome prognostication.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 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".