Cerebral Oxygenation Monitoring Using Near-Infrared Spectroscopy in Delivery Room Resuscitation of Neonates: A Systematic Review
Bibliographic record
Abstract
This systematic review evaluates near-infrared spectroscopy (NIRS) in monitoring cerebral oxygenation during neonatal delivery room resuscitation. The objective was to assess its impact on cerebral regional oxygen saturation (CrSO₂), intraventricular hemorrhage (IVH) incidence, and time to stabilization compared to standard protocols. Using a Population, Intervention, Comparison, Outcome (PICO) framework, we searched PubMed, Embase, Cochrane Library, Scopus, and Web of Science from January 2010 to July 2025 for randomized controlled trials (RCTs) and observational studies in English, focusing on term and preterm neonates requiring resuscitation. Exclusions included non-human studies, non-English articles, case reports, and non-delivery room settings. Two reviewers screened 3,500 studies, with 700 remaining after deduplication, and four high-quality primary studies were included after rigorous selection. Quality was assessed using the Cochrane Risk of Bias Tool for RCTs and the Newcastle-Ottawa Scale for observational studies. Due to heterogeneity in study designs, NIRS devices, and outcomes, a narrative synthesis was conducted. Results showed NIRS feasibility, with CrSO₂ rising from 36-37% at one minute to 52-84% by seven to 15 minutes in preterm neonates. One RCT reported a 55.4% reduction in cerebral hypoxia burden (p<0.05), and one cohort study showed lower CrSO₂ in neonates with IVH (p=0.003). IVH incidence was 4-12% (50% in one study), with limited control group data. Stabilization time data were limited. No studies addressed India's high-risk neonatal population or long-term outcomes. NIRS shows promise for optimizing cerebral oxygenation and identifying IVH risk, but larger RCTs, standardized protocols, and India-specific studies are needed.
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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 | 0.008 | 0.035 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.008 | 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) | 0.003 | 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".