Arterial flow patterns in healthy transitioning near-term neonates
Bibliographic record
Abstract
OBJECTIVE: Significant haemodynamic changes occur at delivery impacting organ blood flow distribution. We aimed to characterise Doppler indices patterns over time in three different organs (brain, gut and kidney) and test them as measures of vascular resistance. DESIGN: Observational cohort study. Serial Doppler interrogations of the anterior cerebral, superior mesenteric and renal arteries within 2 hours, 2-6, and 24 hours of life, in combination with central haemodynamic data. PATIENTS: Healthy, near-term (>36 weeks of gestation) neonates. OUTCOME MEASURES: Pulsatility (PI) and Resistance Indices (RI) patterns and organ-specific conductances, detailed echocardiographic haemodynamic measures. RESULTS: Twenty-one babies were studied. Doppler morphology and adaptation patterns were distinctly different between the organs (brain, gut and kidney) supporting autonomous vascular regulatory effects. The PI differentiated especially between kidney and other organ flow consistently over time. PI and RI for all three organs decreased. The variance in organ conductance did not explain the variance in 1/PI, indicating that PI is not a measure of resistance. Superior mesenteric artery had the highest velocity with 72 cm/s. Non-invasively acquired pilot serial values in a normal population are given. Patent ductus arteriosus flow remained open at discharge for 36%. CONCLUSIONS: Haemodynamic transitioning patterns assessed by serial Dopplers in healthy near-term neonates differ in brain, gut and kidney: Doppler waveform morphology differs, and PI differentiates renal Doppler morphology, compared with the other organs. While PI and RI decline for all organs, they do not measure resistance. Brain artery velocity increases, mesenteric perfusion is variable and renal Vmax decreases.
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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 | 0.000 | 0.002 |
| 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 | 0.000 | 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 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".