Ultrasound Assessment of Gastric Content and Volume
Bibliographic record
Abstract
BACKGROUND: Aspiration of gastric contents can be a serious perioperative complication, attributing up to 9% of all anesthesia-related deaths. However, there is currently no practical, noninvasive bedside test to determine gastric content and volume in the perioperative period. METHODS: The current study evaluates the feasibility of using bedside ultrasonography for assessing gastric content and volume. In the pilot phase, 18 healthy volunteers were examined to assess the gastric antrum, body, and fundus in cross-section in five prandial states: fasting and after ingestion of 250 mL of water, 500 mL of water, 500 mL of effervescent water, and a solid meal. In the phase II study, the authors concentrated on ultrasound examination of the gastric antrum in 36 volunteers for whom regression analysis was used to determine the correlation between gastric volume and antral cross-sectional area. RESULTS: The gastric antrum provided the most reliable quantitative information for gastric volume. The antral cross-sectional area correlated with volumes of up to 300 mL in a close-to-linear fashion, particularly when subjects were in the right lateral decubitus position. Sonographic assessment of the gastric antrum and body provides qualitative information about gastric content (empty or not empty) and its nature (gas, fluid, or solid). The fundus was the gastric area least amenable to image and measure. CONCLUSIONS: Our preliminary results suggest that bedside two-dimensional ultrasonography can be a useful noninvasive tool to determine gastric content and volume.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".