End-organ saturations correlate with aortic blood flow estimates by echocardiography in the extremely premature newborn – an observational cohort study
Bibliographic record
Abstract
BACKGROUND: Near-infrared spectroscopy (NIRS) measures of cerebral saturation (Csat) and renal saturation (Rsat) in extreme premature newborns may be affected by systemic blood flow fluctuations. Despite increasing clinical use of NIRS to monitor tissue saturation in the premature infant, validation of NIRS measures as a correlate of blood flow is still needed. We compared echocardiography (ECHO) derived markers of ascending aorta (AscAo) and descending aorta (DesAo) blood flow with NIRS measurements obtained during the ECHO. METHODS: Newborns < 29 weeks' gestation (2013-2017) underwent routine NIRS monitoring. Csat, Rsat and systemic saturation at the time of ECHO were retrospectively analyzed and compared with Doppler markers of aortic flow. Renal and cerebral fractional tissue oxygen extraction (rFTOE and cFTOE, respectively) were calculated. Mixed effects models evaluated the association between NIRS and Doppler markers. RESULTS: Forty-nine neonates with 75 Csat-ECHO and 62 Rsat-ECHO observations were studied. Mean post-menstrual age was 28.3 ± 3.8 weeks during the ECHO. Preductal measures including AscAo velocity time integral (VTI) and AscAo output were correlated with Csat or cFTOE, while postductal measures including DesAo VTI, DesAo peak systolic velocity, and estimated DesAo output were more closely correlated with Rsat or rFTOE. CONCLUSIONS: NIRS measures are associated with aortic blood flow measurements by ECHO in the extremely premature population. NIRS is a tool to consider when following end organ perfusion in the preterm infant.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".