Carbon dioxide and cardiac output as major contributors to cerebral oxygenation during apnoeic oxygenation
Bibliographic record
Abstract
Abstract Apnoeic oxygenation has experienced a resurgence in interest in critical care and perioperative medicine. However, its effect on cerebral oxygenation and factors influencing it, have not yet been investigated in detail. By using near-infrared spectroscopy, we intended to provide further evidence for the safety of apnoeic oxygenation and to increase our understanding of the association between cerebral perfusion, haemodynamic, respiratory and demographic factors. In this secondary analysis of a prospective randomized controlled noninferiority trial, we recruited 125 patients, who underwent surgery under general anaesthesia with neuromuscular blockade. Arterial blood samples were taken every 2 min for a total of 15 min under apnoeic oxygenation with 100% oxygen. Near-infrared spectroscopy and cardiac output were continuously measured. Statistical analysis was performed using uni- and multivariable statistics. Ninety-one complete data sets were analysed. In six patients the SpO2 fell below 92% (predefined study termination criterion). The significant average increase of cerebral oxygenation was 0.5%/min and 2.1 mmHg/min for the arterial pressure of carbon dioxide (paCO2). The median cardiac output increased significantly from 5.0 l/min (IQR 4.5–6.0) to 6.5 l/min (IQR 5.7–7.5). The most significant effect on cerebral oxygenation was exhibited by the variable paCO2 and non-specific patient factors, followed by cardiac output and paO2. Apnoeic oxygenation proves to have a high safety profile while significantly increasing cerebral oxygenation, paCO2 and cardiac output. In reverse, NIRS might act as a reliable clinical surrogate of paCO2 and cardiac output during stable arterial oxygenation.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".