Predictive Utility of Near-Infrared Spectroscopy for the Outcomes of Hypoxic-Ischemic Encephalopathy: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
This systematic review and meta-analysis aimed to assess the utility of near-infrared spectroscopy (NIRS) in predicting the perinatal outcomes of neonates with hypoxic-ischemic encephalopathy (HIE). We conducted a literature search on Medline via PubMed, Web of Science, Scopus, and CENTRAL Library. We included studies that utilized early NIRS monitoring to study the accuracy of NIRS in predicting the perinatal outcomes of neonates with hypoxic-ischemic encephalopathy. Nine studies that met our eligibility criteria were included. These studies were published between 2012 and 2023. In this meta-analysis, no significant differences in regional cerebral oxygen saturation (cSpO2) were found between normal and abnormal groups at 12 hours (MD = 0.21, 95% CI: -6.39 to 6.82, P = 0.95) and 24 hours (MD = -1.96, 95% CI: -6.95 to 3.03, P = 0.44). However, at 48 hours, cSpO2 was significantly lower in the normal group (MD = -4.9, 95% CI: -5.91 to -3.89, P < 0.00001). At 72 hours, our analysis revealed a significant difference with lower cSpO2 in the normal group (MD = -3.0, 95% CI: -5.5 to -0.5, P = 0.02). Regarding cerebral fractional tissue oxygen extraction (FTOE), no significant differences were observed at 12 hours (MD = 0.03, 95% CI: -0.02 to 0.09, P = 0.24). After 24 hours, the normal group exhibited lower FTOE (MD = -0.03, 95% CI: -0.04 to -0.01, P < 0.001), while after 48 hours, the normal group had higher FTOE (MD = 0.07, 95% CI: 0.04 to 0.10, P < 0.0001). Early cerebral NIRS monitoring is beneficial in predicting the outcomes of HIE in term neonates. Our analysis showed that several NIRS parameters, such as regional cSpO2 and cerebral FTOE, are significantly associated with adverse outcomes in the first 72 hours of birth.
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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 | 0.013 | 0.032 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.045 |
| Bibliometrics | 0.007 | 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".