Severity of hypoxic ischemic encephalopathy and heart rate variability in neonates: a systematic review
Bibliographic record
Abstract
Abstract Background Several studies have investigated heart rate variability (HRV) as a biomarker for acute brain injury in hypoxic ischemic encephalopathy (HIE). However, the current evidence is heterogeneous and needs further reviewing to direct future studies. We aimed to systematically review whether HIE severity is associated with HRV. Methods This systematic review was conducted according to the preferred reporting items for systematic review and meta analyses (PRISMA). We included studies comparing neonates with severe or moderate HIE with neonates with mild or no HIE with respect to different HRV measures within 7Â days of birth. Article selection and quality assessment was independently performed by two reviewers. Risk of bias and strength of evidence was evaluated by the Newcastle-Ottawa scale (NOS) and the Grading of Recommendations Assessment, Development and Evaluation (GRADE). Results We screened 1187 studies. From these, four observational studies with 248 neonates were included. For all HRV measures, the strength of evidence was very low. Neonates with severe or moderate HIE showed a reduction in most HRV measures compared to neonates with mild or no HIE with a greater reduction in those with severe HIE. Conclusions Moderate and severe HIE was associated with a reduction in most HRV measures. Accordingly, HRV is a potential biomarker for HIE severity during the first week of life. However, the uncertainty calls for more studies.
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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 | 0.011 | 0.074 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.009 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".