Reliability of ultrasound gastric volume assessment for the prevention of aspiration pneumonia
Bibliographic record
Abstract
We have developed and validated a mathematical model (Volume = 27 + 14.6 x Right-lat CSA - 1.28 age) that accurately predicts gastric fluid volume based on a cross-sectional area of the gastric antrum obtained with 2D ultrasound in the right lateral decubitus position (Right-lat CSA). An accurate assessment of gastric volume can help tailor anesthetic or airway management to prevent aspiration penumonia in intensive care or anesthetic practice. We hypothesize that ultrasound gastric volume assessment has substantial intra-rater and inter-rater reliability (intraclass correlation coefficient > 0.6). We hereby present interim data (n=8) from a prospective, randomized, blinded study (final n=24). Three blinded raters perfomed bedside ultrasound assessment for determination of gastric fluid volume on subjects randomized to ingest 0 to 400 mL of apple juice in 100 mL intervals after an 8 hour fasting period. A standardized scanning protocol was used. The procedure was repeated after 24 hours on the same subjects for determination of intra-rater reliability. Preliminary results suggest there is sustantial intra-rater reliability (ICC = 0.75) and almost perfect inter-rater reliability (ICC = 0.84). In conlcusion, our preliminary results suggest that bedside sonographic assessment of gastric volume has substantial to almost perfect reliability.
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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 | 0.030 | 0.074 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".