How to Monitor the Brain during Immediate Neonatal Transition and Resuscitation: A Systematic Qualitative Review of the Literature
Bibliographic record
Abstract
BACKGROUND: The brain is vulnerable to injury and dysfunction during transition after birth in neonates. Clinical assessment of the neurological status immediately following birth is difficult, especially during resuscitation. OBJECTIVE: Our aim was to review physiological monitoring of the brain during immediate postnatal transition - the first 15 min after birth. METHODS: A systematic search of PubMed and EMBASE was performed using the following terms: newborn, neonate, neonates, transition, after-birth, delivery room, cerebral, brain, monitoring, neurology, oxygenation, saturation, activity, imaging, perfusion, Doppler, and blood flow. Additional articles were identified by manual search of cited references. Only human studies describing cerebral changes during the first 15 min after birth were included. RESULTS: Six studies were identified, which described sequential measurements of cerebral perfusion using Doppler sonography, one of these in combination with continuous monitoring of cerebral tissue oxygenation with near-infrared spectroscopy (NIRS). A further 15 studies were identified that used NIRS to continuously monitor cerebral tissue oxygenation. In one study, cerebral activity was continuously monitored with an additional amplitude-integrated encephalogram. CONCLUSION: Monitoring the brain provides additional information during immediate transition and may help to guide resuscitation. Doppler sonography is technically challenging during resuscitation and is therefore of limited value. NIRS provides continuous monitoring and is feasible even in very-low-birth-weight infants. In the future, an amplitude-integrated encephalogram might give further information on the status of the brain, but before any of these modalities can routinely be recommended during neonatal resuscitation, clinical trials targeting stable brain function parameters 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 | 0.018 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.022 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.003 |
| 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".