Snorkel breathing technique in anesthesia: A narrative review
Bibliographic record
Abstract
The purpose of this narrative review is to investigate the existing evidence supporting the use of the snorkel breathing technique in anesthesia practice. In addition, this review aims to identify certain patient scenarios where the snorkel breathing approach can be appropriate. A total of nine articles relevant to anesthesia and airway management were retrieved using the search strategy. A review of the final papers found that the snorkel breathing technique offers a few advantages over using a regular face mask in the operating room. The snorkel or mouthpiece breathing approach can be as effective as the traditional face mask for successful preoxygenation, as defined by an end-tidal oxygen concentration greater than 0.9. The snorkel breathing approach can be a viable option in patient populations where face mask application is challenging. It also has greater acceptance than the face mask among patients and healthy volunteers. Another advantage of the snorkel breathing technique is that it can be utilized to provide apneic oxygenation during rapid sequence induction using short-acting neuromuscular blocking drugs, reducing the use of plastic (face mask). For older children and claustrophobic adults who are apprehensive about masks, snorkel gas induction can make anesthesia induction smoother and more pleasant. The snorkel breathing technique has potential application in certain anesthetic scenarios, and this narrative review also incorporates the author's clinical experience with it. To validate the effect of this novel technique (snorkel preoxygenation and snorkel gas induction) on patient-centered outcomes, more randomized controlled trials are required.
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".