ASRA pain medicine narrative review and expert practice recommendations for gastric point-of-care ultrasound to assess aspiration risk in medically complex patients undergoing regional anesthesia and pain procedures
Bibliographic record
Abstract
Gastric point-of-care ultrasound (POCUS) may offer clinical value in assessing aspiration risk among medically complex patients undergoing regional anesthesia and pain procedures. While the American Society of Anesthesiologists (ASA) preoperative fasting guidelines primarily apply to healthy individuals, medically complex populations often present with differing gastric emptying and aspiration risk. This narrative review, conducted by the American Society of Regional Anesthesia and Pain Medicine (ASRA-PM), adhered to PRISMA guidelines and was registered with PROSPERO. It focused on seven medically complex patient groups: those who are pregnant, obese, diabetic, have gastroesophageal reflux disease (GERD), are receiving emergency care, are enterally fed, or are taking GLP-1 receptor agonists (GLP-1RA). Study quality was assessed using the Mixed Methods Appraisal Tool (MMAT). Practice recommendations were developed using an iterative expert consensus process, with final recommendations based on evidence strength, clinical relevance, and expert agreement. Findings support the use of gastric POCUS in patients in active labor, those undergoing urgent cesarean sections, and those with diabetes. Conditional support is given for obesity, emergency care, enteral feeding, and GLP-1RA use. Routine use is not recommended in non-laboring pregnancies, elective cesarean delivery, or GERD. While gastric POCUS may aid with aspiration risk evaluation, its use should complement clinical judgment. Implementation may be limited by practical and training constraints, requiring individualized decision-making. These recommendations serve as a foundation for future research and potential clinical guideline development. PROSPERO registration number: CRD42023445927.
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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 | 0.089 | 0.280 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.008 |
| Bibliometrics | 0.015 | 0.009 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".