Impact of ventilation strategies during chest compression. An experimental study with clinical observations
Bibliographic record
Abstract
The optimal ventilation strategy during cardiopulmonary resuscitation (CPR) is unknown. Chest compression (CC) generates circulation, while during decompression, thoracic recoil generates negative pressure and venous return. Continuous flow insufflation of oxygen (CFI) allows noninterrupted CC and generates positive airway pressure (Paw). The main objective of this study was to assess the effects of positive Paw compared with the current recommended ventilation strategy on intrathoracic pressure (P(IT)) variations, ventilation, and lung volume. In a mechanical model, allowing compression of the thorax below an equilibrium volume mimicking functional residual capacity (FRC), CC alone or with manual bag ventilation were compared with two levels of Paw with CFI. Lung volume change below FRC at the end of decompression and P(IT), as well as estimated alveolar ventilation, were measured during the bench study. Recordings were obtained in five cardiac arrest patients to confirm the bench findings. Lung volume was continuously below FRC, and as a consequence P(IT) remained negative during decompression in all situations, including with positive Paw. Compared with manual bag or CC alone, CFI with positive Paw limited the fall in lung volume and resulted in larger positive and negative P(IT) variations. Positive Paw with CFI significantly augmented ventilation induced by CC. Recordings in patients confirmed a major loss of lung volume below FRC during CPR, even with positive Paw. Compared with manual bag ventilation, positive Paw associated with CFI limits the loss in lung volume, enhances CC-induced positive P(IT), maintains negative P(IT) during decompression, and generates more alveolar ventilation.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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 | 0.000 |
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".