P28.08: The relative utility of lowest versus latest <scp>CPR</scp> for identifying late onset <scp>IUGR</scp> and predicting requirement for admission to <scp>NICU</scp>
Bibliographic record
Abstract
To determine the utility of worst versus latest Doppler for identifying late-onset IUGR. Umbilical artery pulsatility index (UA PI) and cerebroplacental ratio (CPR) measured after 30 weeks' gestation age (GA) were collected to determine the lowest CPR and highest UA PI (most abnormal Doppler) for each patient. IUGR was diagnosed if birth weight < 3rd centile or > 20 centile drop in fetal weight was demonstrated after 30 weeks. The performance of worst Doppler markers versus latest Doppler measurements for detecting IUGR were compared using ROC curves. Doppler and newborn data were collected from 38 IUGR and 41 normal pregnancies. The mean GA when the lowest CPR was obtained was earlier in IUGR patients (P = 0.02). Highest UA PI, latest UA PI and latest CPR were obtained at similar GA in the two groups. There was a significantly larger area under the ROC curve for the lowest CPR for detecting IUGR compared to the latest CPR (P = 0.04) and the latest UA PI (P = 0.004). The highest UA PI also performed better than the latest UA PI (P = 0.04). As the lowest CPR decreased, the newborn was more likely to be admitted to the neonatal intensive care unit (P = 0.01).The other three Doppler parameters were not associated with NICU admission. Supporting information can be found in the online version of this abstract Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".