Gastric Sonography in the Severely Obese Surgical Patient
Bibliographic record
Abstract
BACKGROUND: Gastric ultrasonography allows qualitative and quantitative assessment of gastric contents and volume in nonobese subjects. In this study, we sought to determine the feasibility of gastric ultrasound in severely obese patients (body mass index [BMI] ≥35 kg/m). We defined feasibility as the ability to identify a full cross section of the gastric antrum in at least 80% of subjects when imaged in the right lateral decubitus position. METHODS: This was a prospective cohort study on fasted surgical patients with BMI >35 kg/m. The primary outcome measure was the feasibility of gastric sonography. Secondary outcomes included the distribution of antral grade following an existing 3-point grading system. In addition, the antral cross-sectional area (CSA) and gastric volumes in this cohort were compared with historical data from a published study in nonobese individuals. Time to image capture, antral wall thickness, and depth of the antrum are also reported. RESULTS: Sixty patients (BMI range 35.1-68.7) were studied. The antrum was identified in 95% of subjects in the right lateral decubitus (95% CI, 0.86-0.99) and 90% of subjects in the supine position. Definition of antral grade (0-2) was possible in 88.3% (95% CI, 0.77-0.95) of cases. As expected, antral grade correlated with antral CSA and gastric volumes (P < 0.0001). When compared with historical data, our results suggest that severely obese patients have a larger baseline CSA and gastric volume than nonobese patients (P < 0.001) but a similar gastric volume per unit of weight (P = 0.141). CONCLUSIONS: Gastric ultrasound assessment is feasible in fasted severely obese subjects. Our data also suggest that obese individuals present larger antral size and gastric volume than their nonobese counterparts.
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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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".