Monitoring Fetal Electrocortical Activity during Labour for Predicting Worsening Acidemia: A Prospective Study in the Ovine Fetus Near Term
Bibliographic record
Abstract
BACKGROUND: Severe fetal acidemia during labour with arterial pH below 7.00 is associated with increased risk of hypoxic-ischemic brain injury. Electronic fetal heart rate (FHR) monitoring, the mainstay of intrapartum surveillance, has poor specificity for detecting fetal acidemia. We studied brain electrical activity measured with electrocorticogram (ECOG) in the near term ovine fetus subjected to repetitive umbilical cord occlusions (UCO) inducing FHR decelerations, as might be seen in human labour, to delineate the time-course for ECOG changes with worsening acidemia and thereby assess the potential clinical utility of fetal ECOG. METHODOLOGY/PRINCIPAL FINDINGS: Ten chronically catheterized fetal sheep were studied through a series of mild, moderate and severe UCO until the arterial pH was below 7.00. At a pH of 7.24 ± 0.04, 52 ± 13 min prior to the pH dropping <7.00, spectral edge frequency (SEF) increased to 23 ± 2 Hz from 3 ± 1 Hz during each FHR deceleration (p<0.001) and was correlated to decreases in FHR and in fetal arterial blood pressure during each FHR deceleration (p<0.001). CONCLUSIONS/SIGNIFICANCE: The UCO-related changes in ECOG occurred in advance of the pH decreasing below 7.00. These ECOG changes may be a protective mechanism suppressing non-essential energy needs when oxygen supply to the fetal brain is decreased acutely. By detecting such "adaptive brain shutdown," the need for delivery in high risk pregnant patients may be more accurately predicted than with FHR monitoring alone. Therefore, monitoring fetal electroencephalogram (EEG, the human equivalent of ECOG) during human labour may be a useful adjunct to FHR monitoring.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".