Gastric Volume and Antral Area in the Fasting State: A Meta-analysis of Individual Patient Data
Bibliographic record
Abstract
BACKGROUND: Pulmonary aspiration of gastric content is a serious anesthetic complication. Gastric point-of-care ultrasound can determine the type and volume of gastric content when clinical information is equivocal. However, a cutoff value of either antral cross-sectional area or volume that may be considered as the upper limit of normal in fasting subjects is still controversial. The aim of this study is to characterize the distribution of baseline antral area and volume in fasting adult subjects and to identify an upper limit (95th percentile) of these distributions. METHODS: The authors conducted a meta-analysis of individual participant data of primary studies from an academic research network of investigators collaborating in gastric ultrasound. Studies between January 2009 and December 2020 were included. RESULTS: Twelve primary studies met inclusion criteria and were included in the analysis with a sample size of 1,203 subjects. The 95th percentile of area values (measured in the right lateral decubitus) was 9.9 cm2 (95% CI, 9.4 to 10.4), and of volume, 2.3 ml/kg (95% CI, 2.3 to 2.4). In addition, an antrum grade 0 or 1 indicates a 98% probability of an antral area below the 95th percentile. CONCLUSIONS: An area of 10 cm2 measured in the right lateral decubitus could be a simple, data-driven upper limit of antral area that could serve as a surrogate of upper limit of normal gastric volume values in fasting adults. These results are limited by the highly selected sampling of the studies included.
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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 | 0.014 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.032 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".