Abstract 324: Assessment of Optimal Chest Compression Depth to Optimize Cardiopulmonary Resuscitation: A Randomized Controlled Trial
Bibliographic record
Abstract
Introduction: Neonatal chest compression (CC) should be performed to a 1/3 anterior-posterior (AP) chest diameter depth, however, the optimal AP depth is unknown. Hypothesis: We hypothesized that in asphyxiated neonatal piglets a 40% AP depth compared to 1/3, or 1/4 AP depth will reduce time to achieve return of spontaneously circulation and improve survival. Methods: Newborn piglets (n=8/group) were anesthetized, intubated, instrumented, and exposed to 45-minute normocapnic hypoxia followed by asphyxia and cardiac arrest. Piglets were randomly allocated to four intervention groups (“AP 12.5% depth”, “AP 1/4 depth”, “AP 1/3 depth” or “AP 40% depth“). CCs were performed using an automated CC machine with a rate of 90/min. Hemodynamic and respiratory parameters were continuously measured. Results: Median (IQR) time to return of spontaneously circulation was 70 (60-117), 85 (72-90), 90 (90-130), and 600 (600-600) sec with AP 40% depth, AP 1/3 depth, AP 1/4 depth, AP 12.5% depth, respectively. No piglet in the AP 12.5% depth group achieved ROSC while the short-term survival (1h) in the other groups was 100%. Systolic and diastolic blood pressure, central venous pressure, carotid blood flow, tidal volume, and minute ventilation increased with increasing AP depth. Conclusions: Time to return of spontaneously circulation and survival was similar between 1/4, 1/3, and 40% AP depth, while 12.5% AP depth did not result in return of spontaneously circulation. Hemodynamic and respiratory parameters improved with increasing AP depth suggesting that 40% AP depth might provide improved organ perfusion and oxygen delivery.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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".