Chest Compression in Neonatal Cardiac Arrest: Cerebral Blood Flow Measurements in Experimental Models
Bibliographic record
Abstract
The main aim of this paper was to provide an overview of studies that measured cerebral blood flow (CBF), directly or indirectly, during chest compression (CC) in neonatal animals. Our main research question was: how did different ways of performing CC influence CBF. We also aimed to discuss strengths and limitations of different methods for measuring CBF. Based on a search in Medline Ovid, we identified three studies in piglets that investigated different CC:ventilation (C:V) ratios, as well as three piglet studies investigating continuous CC with asynchronous ventilation. CBF was measured indirectly in all studies by means of carotid artery (CA) flow and regional cerebral oxygenation (rcSO2). The CA provides flow to the brain, but also to extracerebral structures. The relative sizes of the internal and external carotid arteries and their flow distributions are species-dependent. rcSO2 is a non-invasive continuous measure, but does not only reflect CBF, but also cerebral blood volume and the metabolic rate of oxygen in the brain. Continuous CC with asynchronous ventilation at a CC rate of 120/min, and combining CC with a sustained inflation (four studies in piglets and one in lambs) provided a faster CBF recovery compared with the standard 3:1 C:V approach.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".