Left ventricle chest compression improves ETCO2, blood pressure, and cerebral blood velocity in a swine model of cardiac arrest and cardiopulmonary resuscitation
Bibliographic record
Abstract
During cardiopulmonary resuscitation (CPR), high quality chest compressions are critical to organ perfusion, especially the brain. Yet, the optimal location for chest compressions is unclear. It was hypothesized that compared with the standard chest compression (SCC) location, left ventricle chest compressions (LVCCs) would result in greater end-tidal carbon dioxide (ETCO2), blood pressure (BP), and cerebral blood velocity (CBV) during CPR in swine. Female Landrace swine (N = 32; 35 ± 2 kg) underwent two mins of untreated asphyxiated cardiac arrest (CA). Thereafter, swine were treated with three 2-min cycles of either SCC or LVCC mechanical basic life support CPR (LUCAS 3). ETCO2 (in-line sampling), BP (arterial catheter line), and CBV (transcranial Doppler) were measured during the pre-CA, untreated-CA, and CPR-treated phases. ETCO2, BP, and CBV were similar between groups at pre- and during untreated-CA (P ≥ 0.188). During CPR, ETCO2 (36 ± 6 versus 24 ± 10 mmHg, P < 0.001), mean arterial BP (MAP; 49 ± 9 versus 37 ± 9 mmHg, P = 0.002), and CBV (11 ± 5 versus 5 ± 2 cm/s, P < 0.001) were significantly greater in the LVCC versus SCC group. Moreover, a greater proportion of animals obtained targets for ETCO2 (ETCO2 ≥ 20 mmHg; 52 % (17/33) versus 100 % (32/32), P < 0.001) and diastolic BP (DBP ≥ 25 mmHg; 82 % (33/40) versus 97 % (48/49), P = 0.020) in the LVCC versus SCC group. Indicators of cardiac output, BP, and cerebral perfusion during CPR were greatest in the LVCC group, suggesting the quality of chest compressions during BLS CPR may be improved by performing compressions over the left ventricle compared to the centre of the chest.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".