224 aEEG and NIRS During Transition after Birth
Bibliographic record
Abstract
Background and aims Easily applicable non-invasive devices to monitor cerebral activity and oxygenation continuously during neonatal transition and resuscitation are lacking. We aimed to identify a method of directly monitoring cerebral activity and oxygenation during transition and resuscitation after birth. Methods Neonates >34 weeks gestation born via caesarean section were included. Cerebral activity was continuously measured with amplitude integrated EEG (aEEG) and cerebral oxygenation (rSO2) with near-infrared-spectroscopy during the first ten minutes after birth. For quantitative analysis of aEEG the mean minimum amplitude (Vmin) and maximum amplitude (Vmax) was determined at every minute. Neonates with normal transition were compared to neonates with need of resuscitation. Results Out of 224 eligible neonates 63 were included and 46 had reliable measurements: 31 with normal transition and 15 in need of resuscitation. Neonates with normal transition showed higher values for Vmin in the third minute and higher values for Vmax in the third and fourth minute compared to minute 10. Neonates requiring respiratory support had lower values for Vmin in the ninth minute compared to minute 10. In neonates with normal transition rSO2 values during the first six minutes were lower when compared to minute 10. rSO2 values in neonates requiring respiratory support remained lower over the first eight minutes when compared to minute 10. Conclusions This is the first study demonstrating the feasibility of aEEG and rSO2 monitoring during neonatal transition. The cerebral activity pattern in compromised infants requiring resuscitation was different when compared to infants with normal transition.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".