Bridging the notch: quantification of the end diastolic notch to better predict fetal growth restriction
Bibliographic record
Abstract
PURPOSE: We aimed to evaluate several quantitative methods to describe the diastolic notch (DN) and compare their performance in the prediction of fetal growth restriction. MATERIALS AND METHODS: Patients who underwent a placental scan at 16-26 weeks of gestation and delivered between Jan 2016 and Dec 2020 were included. The uterine artery pulsatility index was measured for all of the patients. In patients with a DN, it was quantified using the notch index and notch depth index. Odds ratios for small for gestational age neonates (defined as birth weight <10th and <5th percentile) were calculated. Predictive values of uterine artery pulsatility, notch, and notch depth index for fetal growth restriction were calculated. RESULTS: Overall, 514 patients were included, with 69 (13.4%) of them delivering a small for gestational age neonate (birth weight<10th percentile). Of these, 20 (20.9%) had a mean uterine artery pulsatility index >95th percentile, 13 (18.8%) had a unilateral notch, and 11 (15.9%) had a bilateral notch. 16 patients (23.2%) had both a high uterine artery pulsatility index (>95th percentile) and a diastolic notch. Comparison of the performance between uterine artery pulsatility, notch, and notch depth index using receiver operating characteristic curves to predict fetal growth restriction <10th percentile found area under the curve values of 0.659, 0.679, and 0.704, respectively, with overlapping confidence intervals. CONCLUSION: Quantifying the diastolic notch at 16-26 weeks of gestation did not provide any added benefit in terms of prediction of neonatal birth weight below the 10th or 5th percentile for gestational age, compared with uterine artery pulsatility index.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".