Gastric Ultrasound for the Regional Anesthesiologist and Pain Specialist
Bibliographic record
Abstract
This article in our series on point-of-care ultrasound (US) for the regional anesthesiologist and pain management specialist describes the emerging role of gastric ultrasonography. Although gastric US is a relatively new point-of-care US application in the perioperative setting, its relevance for the regional anesthesiologist and pain specialist is significant as our clinical practice often involves providing deep sedation without a secured airway. Given that pulmonary aspiration is a well-known cause of perioperative morbidity and mortality, the ability to evaluate for NPO (nil per os) status and risk stratify patients scheduled for anesthesia is a powerful skill set. Gastric US can provide valuable insight into the nature and volume of gastric content before performing a block with sedation or inducing anesthesia for an urgent or emergent procedure where NPO status is unknown. Patients with comorbidities that delay gastric emptying, such as diabetic gastroparesis, neuromuscular disorders, morbid obesity, and advanced hepatic or renal disease, may potentially benefit from additional assessment via gastric US before an elective procedure. Although gastric US should not replace strict adherence to current fasting guidelines or be used routinely in situations when clinical risk is clearly high or low, it can be a useful tool to guide clinical decision making when there is uncertainty about gastric contents.In this review, we will cover the relevant scanning technique and the desired views for gastric US. We provide a methodology for interpretation of findings and for guiding medical management for adult patients. We also summarize the current literature on specific patient populations including obstetrics, pediatrics, and severely obese subjects.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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 teacher head, 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".