Ultrasound assessment of gastric volume in the fasted pediatric patient undergoing upper gastrointestinal endoscopy: development of a predictive model using endoscopically suctioned volumes
Bibliographic record
Abstract
BACKGROUND: Aspiration of gastric contents can be a serious anesthetic-related complication. Gastric antral sonography prior to anesthesia may have a role in identifying pediatric patients at risk of aspiration. We examined the relationship between sonographic antral area and endoscopically suctioned gastric volumes, and whether a 3-point qualitative grading system is applicable in pediatric patients. METHODS: Fasted patients presenting to a pediatric hospital for upper gastrointestinal endoscopy were included in the study. Sonographic measurement of the antral cross-sectional area (CSA) in supine (supine CSA) and right lateral decubitus (RLD CSA) position was completed, and the antrum was designated as empty or nonempty. Gastric contents were endoscopically suctioned and measured. Multiple regression analysis was used to fit a mathematical model to estimate gastric volume. RESULTS: One hundred patients (aged 11-216 months) were included. The gastric antrum was measured in 94% and 99% of patients in the supine and RLD positions, respectively. Gastric antral CSA correlated with total gastric volume in both supine (ρ = 0.63) and RLD (ρ = 0.67) positions. A mathematical model incorporating RLD CSA and age (R(2) = 0.60) was determined as the best-fit model to predict gastric volumes. Increasing gastric antral grade (0-2) was associated with increasing gastric fluid volume. CONCLUSION: The results suggest that sonographic assessment of the gastric antrum provides useful information regarding gastric content (empty versus nonempty) and volume (ml·kg(-1) ) in pediatric patients. Results suggest that the three-point grading system may be a valuable tool to assess gastric 'fullness' based on a qualitative exam of the antrum.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".