Diagnostic Accuracy of Point-of-Care Gastric Ultrasound
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 it