3.1 Prediction of Fetal Compromise: The Use of Fetal Doppler Assessment in Normal Pregnancies Prior to Labour
Bibliographic record
Abstract
Introduction Up to 63% of cases of intra-partum hypoxia occur in pregnancies with no antenatal risk factors. Identification before labour of these antenatally normal fetuses at risk of intra-partum hypoxia would enable a more targeted approach to intra-partum care. Methods Five hundred and eleven women with uncomplicated, term, singleton pregnancies, underwent a pre-labour ultrasound assessment. This included measurement of fetal biometry, Umbilical artery, Middle cerebral artery, and Umbilical venous resistance indices. Clinicians managing the labour were blinded to the ultrasound results. Following delivery, case notes were reviewed and intra-partum outcomes correlated with ultrasound findings. Results Infants born by Caesarean section for presumed fetal compromise had the highest Umbilical artery pulsatility index (p = 0.002), the lowest Middle cerebral artery pulsatility index (p < 0.001), the lowest cerebro-umbilical ratio (p < 0.001), the lowest Umbilical venous flow rates (p = 0.003), and the highest cerebral blood flow of any mode of delivery group (p = 0.007). A cerebro-umbilical ratio <10th centile has a positive predictive value of 36% for Caesarean section for presumed fetal compromise. This can be improved to 61.5% by inclusion of the other Doppler parameters. A cerebro-umbilical ratio >90th centile has a 100% negative predictive value. Conclusion Pre labour fetal Doppler assessment can identify fetuses at both high and low risk of subsequent compromise in labour. Current intra-partum monitoring has a high false positive rate, which could be improved by better risk stratification prior to labour. This technique is easily translatable into clinical practise and would allow risk stratification of normal pregnancies prior to labour, enabling a more targeted approach to intra-partum care.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".