Identification of the Dynamic Relationship Between Intrapartum Uterine Pressure and Fetal Heart Rate for Normal and Hypoxic Fetuses
Bibliographic record
Abstract
Labor and delivery are routinely monitored electronically with sensors that measure and record maternal uterine pressure (UP) and fetal heart rate (FHR), a procedure referred to as cardiotocography (CTG). Delay or failure to recognize abnormal patterns in these recordings can result in a failure to prevent fetal injury. We address the challenging problem of interpreting intrapartum CTG in a novel way by modeling the dynamic relationship between UP (as an input) and FHR (as an output). We use a nonparametric approach to estimate the dynamics in terms of an impulse response function (IRF). We apply singular value decomposition to suppress noise, IRF delay, and memory estimation to identify the temporal extent of the response and surrogate testing to assess model significance. We construct models for a database of CTG recordings labeled by outcome, and compare the models during the last 3 h of labor as well as across outcome classes. The results demonstrate that the UP-FHR dynamics can be successfully modeled as an input-output system. Models for pathological cases had stronger, more delayed, and more predictable responses than those for normal cases. In addition, the models evolved in time, reflecting a clinically plausible evolution of the fetal state due to the stress of labor.
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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 | 0.000 |
| 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) | 0.000 | 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".