System-identification noise suppression for intra-partum cardiotocography to discriminate normal and hypoxic fetuses
Why this work is in the frame
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Bibliographic record
Abstract
We construct linear system-identification models of cardiotocography (CTG) data collected during labour and delivery. The models are the impulse response functions (IRFs) of the input-output system relating the uterine pressure (UP) stimulus to the fetal heart rate (FHR) response. We compare models obtained with and without applying noise suppression via the pseudo inverse technique. Finally, to determine the ability of the models to discriminate healthy from hypoxic fetuses, we use the average models as feature vectors of a support-vector-machine (SVM) classifier. Applying the pseudo-inverse resulted in cleaner models with lower variance accounted for (VAF), likely indicative of reduced overfitting. The area under curve of the receiver-operator characteristic (ROC) without applying pseudo-inverse was 0.695 plusmn 0.054. Similar results over a useful operating range of false-positive rates were observed with the pseudo-inverse applied.
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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) | 0.001 | 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 it