Validation of a Mathematical Model for Ultrasound Assessment of Gastric Volume by Gastroscopic Examination
Bibliographic record
Abstract
INTRODUCTION: Pulmonary aspiration of gastric contents is a serious perioperative complication. Previous models of ultrasound gastric volume assessment are preliminary and have not been validated by an external "gold standard." In the present study we propose a more accurate model based on prospective data obtained from 108 patients undergoing bedside gastric sonography and upper gastrointestinal endoscopy (UGE). METHODS: Patients undergoing elective UGE were randomized to ingest one of 6 predetermined volumes of apple juice after an 8-hour fasting period. A cross-sectional area of the antrum in the right lateral decubitus position (Right lat CSA) was measured by a blinded sonographer following a standardized scanning protocol. Gastric fluid was subsequently suctioned under gastroscopic vision during UGE performed by a blinded gastroenterologist and measured to the nearest milliliter. RESULTS: Data from 108 patients suggest that a previously reported model tends to overestimate gastric volume particularly at low volume states. A new best fit mathematical model to predict gastric fluid volume based on measurements of Right lat CSA is presented. This new model built on a more accurate gold standard can be used to estimate gastric volumes from 0 to 500 mL, in nonpregnant adults with body mass index<40 kg/m2. CONCLUSIONS: We report a new prediction model to assess gastric fluid volume using standard 2-dimentional bedside ultrasound that has several advantages over previously reported models.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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".