The Prognostic Weight of Clinical, Biochemical, Electrographic, and Neuroimaging Biomarkers in Perinatal Hypoxic-Ischemic Encephalopathy Spectrum
Bibliographic record
Abstract
OBJECTIVE: To assess the prognostic weight of potential biomarkers in infants within the full spectrum of mild, moderate, and severe hypoxic-ischemic encephalopathy. STUDY DESIGN: This observational study was conducted as a nested substudy of a prospectively collected perinatal hypoxic-ischemic encephalopathy cohort (RECOVER [Remote Early Intervention for Cerebral Palsy to Improve Outcomes Using Virtual Care Following Perinatal Asphyxia] study, ClinicalTrials.gov ID: NCT04913324) at the University of Toronto, Hospital for Sick Children. Clinical, laboratory, electrographic, and neuroimaging biomarkers were longitudinally collected and objectively evaluated using scoring systems. Neurodevelopmental outcomes were assessed at 18 months corrected age using standardized tests. RESULTS: Of the 200 infants included in the cohort, the severity of neonatal encephalopathy was classified as mild in 40 (20%), moderate in 118 (59%), and severe in 42 (21%). Of these infants, 27 (14%) died. In the multivariable model, brain magnetic resonance imaging deep gray matter (DGM) injury subscore was the only prognostic marker that was associated with adverse outcomes (OR 1.73, 95% CI: 1.20 to 2.49; P = .003) after accounting for Apgar score at 10 minutes, pre- and postrewarming Thompson score, presence of a benign clinical course, absence of electrographic background normalization in 48 hours, absence of sleep-wake cycling in 72 hours, and electrographically confirmed seizures. A DGM injury subscore cutoff of 6 demonstrated a sensitivity of 82% and a specificity of 94% with an area under the curve of 0.91 (95% CI: 0.84 to 0.98; P < .001). CONCLUSIONS: The DGM injury subscore emerged as the only independent predictor of adverse outcome at 18 months corrected age, after adjusting for longitudinal clinical, laboratory, electrographic, and neuroimaging biomarkers.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 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".