Abstract 9565: Quantitative Assessment of Neonatal Brain Perfusion by Ultrafast Power Doppler in the Operating Room
Bibliographic record
Abstract
Introduction: The main challenge with cardiopulmonary bypass (CPB) surgery in newborns with congenital heart disease is to prevent ischemic brain injury impacting on the long-term neurodevelopmental outcomes. To date, no quantitative method is available to adapt the CBP parameters for optimal per-operative cerebral perfusion. Ultrafast power Doppler (UPD) is a new portative bedside technique to map and quantify cerebral perfusion. Our objective was to evaluate UPD for quantifying the variation of cerebral perfusion in neonates during CBP surgery. Methods: UPD was performed in 5 newborns with hypoplastic left heart syndrome undergoing a Norwood procedure and 5 age-matched healthy newborns. Using a 6.9 MHz linear array probe (11L, General Electric) connected to a programmable and portable ultrafast ultrasound scanner (Vantage 256, Verasonics Inc.), via a transfontanellar approach, six pre-defined brain imaging planes were acquired in sagittal and coronal views. Assessments were performed pre-operatively, during various perfusions states intra-operatively and 24-48 hours post-operatively. Global and regional cerebral blood volume (CBV) and resistivity index (RI) maps from UPD were obtained. Results: The baseline brain perfusion was the same for both groups (p=0.62). During CPB, whole brain and regional CBV decreases more than 50% compared to baseline and reaching approximately 25% of initial CBV during selective brain perfusion and during the final stages of bypass (Figure). RI maps shows intermittent overall decrease of vascular resistivities below 0.5 on bypass and selective brain perfusion before returning to baseline in post-op, reflecting an overall decrease of the cerebral vascular resistance during CPB. Conclusions: UPD demonstrates variation in cerebral vascular resistance and blood volume in neonates undergoing CPB surgery. UPD paves the road for optimization of CPB parameters in neonates undergoing cardiac surgery.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".