Diagnostic Accuracy of Point-of-Care Gastric Ultrasound
Bibliographic record
Abstract
BACKGROUND: Pulmonary aspiration of gastric contents is associated with significant perioperative morbidity and mortality. Previous studies have investigated the validity, reliability, and possible clinical impact of gastric ultrasound for the assessment of gastric content at the bedside. In the present study, we examined the accuracy (evaluated as sensitivity, specificity, and likelihood ratios) of point-of-care gastric ultrasound to detect a "full stomach" in a simulated scenario of clinical equipoise. METHODS: After a minimum fasting period of 8 hours, 40 healthy volunteers were randomized in a 1:1 ratio to either remain fasted or ingest a standardized quantity of clear fluid or solid. Each subject was randomized twice on 2 independent study sessions at least 24 hours apart. A gastric ultrasound examination was performed by a blinded sonographer following a standardized scanning protocol. Using a combination of qualitative and quantitative findings, the result was summarized in a dichotomous manner as positive (any solid or >1.5 mL/kg of clear fluid) or negative (no solid and ≤1.5 mL/kg of clear fluid) for full stomach. RESULTS: Data from 80 study sessions were analyzed. In this simulated clinical scenario with a pretest probability of 50%, point-of-care gastric ultrasound had a sensitivity of 1.0 (95% confidence interval [CI], 0.925-1.0), a specificity of 0.975 (95% CI, 0.95-1.0), a positive likelihood ratio of 40.0 (95% CI, 10.33-∞), a negative likelihood ratio of 0 (95% CI, 0-0.072), a positive predictive value of 0.976 (95% CI, 0.878-1.0), and a negative predictive value of 1.0 (95% CI, 0.92-1.0). CONCLUSIONS: Our results suggest that bedside gastric ultrasound is highly sensitive and specific to detect or rule out a full stomach in clinical scenarios in which the presence of gastric content is uncertain.
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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 | 0.013 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".