Gastric ultrasound in the third trimester of pregnancy: a randomised controlled trial to develop a predictive model of volume assessment
Bibliographic record
Abstract
Summary Bedside gastric ultrasonography can be performed reliably by anaesthetists to assess gastric content in the peri‐operative period. We aimed to study the relationship between gastric cross‐sectional area, assessed by ultrasound, and volumes of clear fluids ingested by pregnant women. We recruited 60 non‐labouring third‐trimester pregnant women in a randomised controlled and assessor‐blinded study. A standardised scanning protocol of the gastric antrum was performed in the 45° semirecumbent and 45° semirecumbent‐right lateral positions. Subjects were randomly allocated to drink one out of six predetermined volumes of apple juice (0 ml, 50 ml, 100 ml, 200 ml, 300 ml, 400 ml). Qualitative and quantitative assessments at a baseline period after an 8‐h fast, and immediately after the drink, were used to establish the correlation between antral cross‐sectional area and volume ingested. A predictive model to estimate gastric volume was developed. Antral cross‐sectional area in the semirecumbent right lateral position significantly correlated with the ingested volume (Spearman rank correlation = 0.7; p < 0.0001). A cut‐off value of 9.6 cm 2 discriminated ingested volumes ≥ 1.5 ml.kg −1 with a sensitivity of 80%, a specificity of 66.7%, and an area under the curve of 0.82. A linear predictive model was developed for gastric volume based only on antral cross‐sectional area (Volume (ml) = −327.1 + 215.2 × log (cross‐sectional area) (cm 2 )). We conclude that in pregnant women in the third trimester of gestation, the antral cross‐sectional area correlates well with volumes ingested, and this cut‐off value in the semirecumbent right lateral position discriminates high gastric volumes.
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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 | 0.015 | 0.022 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".