Factors affecting the umbilical artery Doppler reference values in the second and third trimesters
Bibliographic record
Abstract
OBJECTIVE: To explore the contribution of selected methodological factors to the heterogeneity in published umbilical artery pulsatility index (UA-PI) reference charts. METHODS: Cross-sectional study of uncomplicated singleton pregnancies that underwent assessment of UA Doppler at a single center. We explored the effects of the cohort characteristics (parity and estimated fetal weight [EFW] centile cut-off) and the statistical modeling approach used to construct the UA-PI centile reference charts. RESULTS: 25 069 UA-PI measurements from 12 394 patients were analyzed. UA-PI centile values decreased as the minimal EFW centile inclusion cut-off of the study cohort increased. Interpretation of UA-PI using charts constructed from fetuses with EFW > 25th or 50th centiles resulted in a higher proportion of examinations with UA-PI > 95th centile compared with the chart based on fetuses with EFW⟩10th centile (11.5% and 13.2% versus 10.6% of the subgroup of small-for-gestational-age fetuses, respectively, P < 0.001). In contrast, parity and the statistical method used to construct the chart had minimal impact on the UA-PI centile reference charts. Considerable heterogeneity was identified among published UA-PI reference charts. CONCLUSION: Variation in the distribution of EFW centiles across the populations used to construct the UA-PI reference chart may contribute to the heterogeneity observed in published charts.
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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 | 0.092 | 0.384 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".