The Effect of Prolonged Face Mask Ventilation on Gastric Insufflation: A Prospective Observational Study.
Bibliographic record
Abstract
BACKGROUND: Pulmonary aspiration is a potentially lethal perioperative complication that can be precipitated by gastric insufflation. Face mask ventilation (FMV), a ubiquitous anesthetic procedure, can cause gastric insufflation. FMV with an inspiratory pressure of 15 cm H2O provides the best balance between adequate pulmonary ventilation and a low probability of gastric insufflation. There is no data about the effects of FMV > 120 seconds. OBJECTIVES: To investigate the effect of prolonged FMV on gastric insufflation. METHODS: We conducted a prospective observational study at a tertiary medical center with female patients who underwent oocyte retrieval surgery under general anesthesia FMV. Pre- and postoperative gastric ultrasound examinations measured the gastric antral cross-sectional area to detect gastric insufflation. Pressure-controlled FMV with an inspiratory pressure of 15 cm H2O was continued from the anesthesia induction until the end of the surgery. RESULTS: The study comprised 49 patients. Baseline preoperative gastric ultrasound demonstrated optimal and good image quality. All supine measurements were feasible. The median duration of FMV was 13 minutes (interquartile range 9-18). In the postoperative period, gastric insufflation was detected in only 2 of 49 patients (4.1%). There was no association between the duration of FMV and delta gastric antral cross-sectional area (β -0.01; 95% confidence interval -0.04 to 0.01, P = 0.31). CONCLUSIONS: Pressure-controlled FMV with an inspiratory pressure of 15 cm H2O carries a low incidence of gastric insufflations, not only as a bridge to a definitive airway but as an alternative ventilation method for relatively short procedures in selective populations.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".