Elevated Cerebral Pressure Passivity Is Associated With Prematurity-Related Intracranial Hemorrhage
Bibliographic record
Abstract
OBJECTIVES: Cerebral pressure passivity is common in sick premature infants and may predispose to germinal matrix/intraventricular hemorrhage (GM/IVH), a lesion with potentially serious consequences. We studied the association between the magnitude of cerebral pressure passivity and GM/IVH. PATIENTS AND METHODS: We enrolled infants <32 weeks' gestational age with indwelling mean arterial pressure (MAP) monitoring and excluded infants with known congenital syndromes or antenatal brain injury. We recorded continuous MAP and cerebral near-infrared spectroscopy hemoglobin difference (HbD) signals at 2 Hz for up to 12 hours/day and up to 5 days. Coherence and transfer function analysis between MAP and HbD signals was performed in 3 frequency bands (0.05-0.25, 0.25-0.5, and 0.5-1.0 Hz). Using MAP-HbD gain and clinical variables (including chorioamnionitis, Apgar scores, gestational age, birth weight, neonatal sepsis, and Score for Neonatal Acute Physiology II), we built a logistic regression model that best predicts cranial ultrasound abnormalities. RESULTS: In 88 infants (median gestational age: 26 weeks [range 23-30 weeks]), early cranial ultrasound showed GM/IVH in 31 (37%) and parenchymal echodensities in 10 (12%) infants; late cranial ultrasound showed parenchymal abnormalities in 19 (30%) infants. Low-frequency MAP-HbD gain (highest quartile mean) was significantly associated with early GM/IVH but not other ultrasound findings. The most parsimonious model associated with early GM/IVH included only gestational age and MAP-HbD gain. CONCLUSIONS: This novel cerebrovascular monitoring technique allows quantification of cerebral pressure passivity as MAP-HbD gain in premature infants. High MAP-HbD gain is significantly associated with GM/IVH. Precise temporal and causal relationship between MAP-HbD gain and GM/IVH awaits further study.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".