Ultrasound to assess gastric content and fluid volume: New kid on the block for aspiration risk assessment in critically ill patients?
Bibliographic record
Abstract
Introduction: Aspiration is associated with significant morbidity and mortality. Fasting guidelines do not apply to critically ill and emergency patients. Gastric point-of-care ultrasound (PoCUS) is a promising approach to assess aspiration risk. This review discusses its feasibility, clinical implications, limitations and future perspectives. Methods: This is a narrative review. A search in PubMed and EMBASE to find relevant articles was performed. Results: Gastric PoCUS provides both qualitative and quantitative information about gastric content and fluid volume. Based on qualitative findings, the antrum is empty or contains fluids or solids. Based on quantitative findings, a fluid volume of up to 500 ml is accurately measured. Gastric PoCUS is feasible in over 90% of subjects. An algorithm for clinical application is presented. Conclusion: Gastric PoCUS is a promising tool to assess gastric content and fluid volume in critical care and emergency patients. Further research on whether the information obtained improves patient outcome is needed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".