Bibliographic record
Abstract
Over the past two decades, diagnostic point-of-care ultrasound (POCUS) has emerged as a rapid and non-invasive bedside tool for addressing clinical inquiries related to gastric content. One emerging concern pertains to patients about to undergo sedation and/or endotracheal intubation: the elevated risk of aspiration from the patient's stomach contents. Aspiration of gastric contents into the lungs poses a serious and potentially life-threatening complication. This occurs more frequently when the stomach is considered "full" and can be affected by the techniques employed for airway management, making it potentially preventable. To mitigate the risk of peri-procedural aspiration, two distinct medical specialties (anesthesiology and critical care medicine) have independently developed techniques to utilize ultrasonography for identifying patients requiring "full stomach" precautions. Due to these separate specialties, the work of each group remains relatively unfamiliar outside its respective field. This article presents descriptions of both techniques for gastric ultrasound. Furthermore, it explains how these approaches can complement each other when one of them falls short. Regarding image acquisition, the article covers the following topics: indications and contraindications, selection of the appropriate probe, patient positioning, and troubleshooting. The article also delves into image interpretation, complete with example images. Additionally, it demonstrates how one of the two techniques can be employed to estimate gastric fluid volume. Lastly, the article briefly discusses medical decision-making based on the findings of this examination.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.341 | 0.267 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".