Gastric ultrasound to guide anesthetic management in elective surgical patients non-compliant with fasting instructions: a retrospective cohort study
Bibliographic record
Abstract
BACKGROUND: Perioperative aspiration leads to significant morbidity and mortality. Standard fasting periods are used to ensure an empty stomach in patients. Anesthesiologists are frequently confronted with cases of dubious adherence to these guidelines. Point-of-care gastric ultrasound is a diagnostic tool that offers information on the type and volume of gastric contents. METHODS: We performed a retrospective analysis of a departmental database containing clinical and sonographic information on elective surgical patients who had been non-compliant to the fasting guidelines. Primary outcome was the incidence of changes in aspiration risk stratification and anesthetic management when a point-of-care gastric ultrasound examination was added to a standard history-based clinical assessment. Secondary outcomes included type of changes (timing of the surgical procedure or change in anesthetic technique) and the incidence of aspiration. Differences in the management plan (history-based versus gastric ultrasound) were tested with McNemar-Bowker's Exact Test of symmetry. RESULTS: Thirty-seven patients met the inclusion criteria. Aspiration risk assessment and anesthetic management changed in 24 cases (64.9%) following gastric ultrasound. Additionally, there was a non-significant difference in the distribution of the pre- and post-test changes in timing (delay, cancel, proceed) (P=0.074) with a trend towards a lower number of surgical cancellations and a higher number of proceeds. No aspirations were documented. CONCLUSIONS: This retrospective study suggests that gastric ultrasound may be a useful diagnostic addition to standard patient assessment in cases of non-compliance to fasting guidelines. It allows to personalize aspiration risk assessment and to tailor anesthetic management to the individual patient.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".