Addition of Nasal Cannula Can Either Impair or Enhance Preoxygenation With a Bag Valve Mask: A Randomized Crossover Design Study Comparing Oxygen Flow Rates
Bibliographic record
Abstract
BACKGROUND: A critical safety component of emergency anesthesia is the avoidance of hypoxemia during the apneic phase of a rapid sequence intubation. Preoxygenation with a bag valve mask (BVM) or anesthetic circuit may be improved with supplemental oxygen by nasal cannula (NC) if there is a mask leak. In addition, NC is recommended for apneic oxygenation after induction and may be placed before preoxygenation. However, the optimum NC flow rate for preoxygenation or whether the presence of NC alone creates a mask leak remains unclear. METHODS: We performed a randomized crossover study on healthy volunteers comparing BVM alone and BVM with NC flow rates of 0 (NC-0), 5 (NC-5), 10 (NC-10), and 15 (NC-15) liters per minute (lpm). Our primary outcome was end-tidal oxygen (ETO2) after 3-minute preoxygenation. RESULTS: There was no difference in ETO2 between NC-15, NC-10, or BVM-only at 3 minutes. NC-0 and NC-5 recorded significantly lower ETO2 at all times compared with NC-15, NC-10, or BVM-only (least difference NC-5, -7% [95% confidence interval {CI}, -4% to -10%), NC-0, 16% [95% CI, 13%-19%]). There was a difference in ETO2 between NC-15 and BVM-only at 1 minute (7%; 95% CI, 5%-9%), but not at 2 or 3 minutes. There was no difference in ETO2 between NC-10 and NC-15. CONCLUSIONS: Our study found that NC at 0 and 5 lpm with a BVM is deleterious to preoxygenation and should be avoided. In addition, a lack of difference between NC-10 and BVM-only demonstrates that NC at flows of at least 10 lpm should not impair the preoxygenation process. While NC-15 may offer a benefit by reaching maximal ETO2 at 1 minute, this would need to be balanced against patient comfort.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".