Gastric Sonography in the Fasted Surgical Patient
Bibliographic record
Abstract
BACKGROUND: Aspiration pneumonia remains a serious anesthetic-related complication. A reliable diagnostic tool to assess gastric volume is currently lacking. We recently demonstrated that gastric sonography can provide reliable qualitative and quantitative information about gastric content and volume in healthy volunteers. In the current study, we performed a prospective qualitative and quantitative analysis of the gastric antrum in 200 fasted patients undergoing elective surgery. METHODS: A standardized gastric scanning protocol was applied before anesthetic induction. Patients were classified following a 3-point grading system based solely on qualitative sonographic assessment of the antrum in the supine and right lateral decubitus positions. RESULTS: Eighty-six patients were classified as grade 0 (empty antrum); 107 patients as grade 1 (minimal fluid volume detected only in the right lateral decubitus position); and 7 patients were classified as grade 2 (antrum clearly distended with fluid visible in both supine and lateral positions). The 3-point grading system correlated with total gastric fluid volume as predicted by a previously reported mathematical model. Essentially grade 0 corresponds to a completely empty stomach, grade 1 corresponds to negligible fluid volumes (16 ± 36 mL) within normal ranges expected for fasted patients, and grade 2 correlates with significantly higher predicted gastric fluid volumes (180 ± 83 mL) beyond previously reported "safe" limits. One patient with a grade 2 antrum had an episode of significant regurgitation of gastric contents on emergence from anesthesia. CONCLUSION: We propose a 3-point grading system based exclusively on qualitative sonographic assessment of the gastric antrum that correlates well with predicted gastric volume. This grading system could be a promising "biomarker" to assess perioperative aspiration risk. Before it can be applied widely to clinical practice, this diagnostic tool needs to be further validated and characterized.
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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".