Voluntary Hyperventilation Before a Rapid-Sequence Induction of Anesthesia Does Not Decrease Postintubation Paco2
Bibliographic record
Abstract
UNLABELLED: To prevent hypercapnia, voluntary hyperventilation is recommended for patients with increased intracranial pressure before the induction of general anesthesia. We sought to determine whether this maneuver results in a lower PaCO2 than breathing 3 min of oxygen 100% by face mask (preoxygenation) after intubation. Thirty patients requiring general anesthesia were randomly assigned to breathe either 3 min of oxygen 100% by face mask (Group P) or 1 min of oxygen 100% followed by 2 min of voluntary hyperventilation with oxygen 100% (Group H). All patients received a standard rapid-sequence induction of anesthesia followed by a 90-s period of apnea. Patients were then tracheally intubated and mechanically ventilated. Five arterial blood gas samples were taken: with room air, after preoxygenation or hyperventilation, after 60 and 90 s of apnea, and after tracheal intubation. Voluntary hyperventilation decreased PaCO2 before rapid-sequence induction (hyperventilation, 30.0 +/- 3.5 mm Hg versus preoxygenation, 37.9 +/- 5.2 mm Hg; P < 0.0001), but after 60 s of apnea, both groups had similar PaCO2 (hyperventilation, 36.1 +/- 3.3 mm Hg versus preoxygenation, 35.6 +/- 3.4 mm Hg; P = 0.673), and no benefit was found after intubation (hyperventilation, 40.5 +/- 3.9 mm Hg versus preoxygenation, 41.4 +/- 2.7 mm Hg; P = 0.603). We conclude that voluntary hyperventilation before rapid-sequence induction does not provide protection against potential hypercapnia during intubation. IMPLICATIONS: Voluntary hyperventilation before anesthesia induction is recommended for patients with increased intracranial pressure to prevent hypercapnia. This randomized, prospective study demonstrated that this maneuver does not result in a lower postintubation PaCO2 than standard preoxygenation.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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".