Point‐of‐care ultrasound use in emergencies: what every anaesthetist should know
Bibliographic record
Abstract
Point-of-care ultrasound has been embraced by anaesthetists as an invaluable tool for rapid diagnosis of haemodynamic instability, to ensure procedural safety and monitor response to treatments. Increasingly available, affordable and portable, with emerging evidence of improved patient outcomes, point-of-care ultrasound has become a valuable tool in the emergency setting. This state-of-the-art review describes the feasibility of point-of-care ultrasound practice, training and maintenance of competence. It also describes the many uses of point-of-care ultrasound for the anaesthetist and describes the most salient point-of-care ultrasound views for anaesthetic emergencies including: undifferentiated shock; hypoxemia; and trauma. Procedural safety is also discussed in addition to relevant important governance aspects. Cardiac function should be assessed using the parasternal long axis, parasternal short basal/mid-papillary/apical, apical four chamber and subcostal four chamber views, and should include a visual estimation of global left ventricular ejection fraction. Other cardiovascular conditions that can be identified using point-of-care ultrasound include: pericardial effusion; cardiac tamponade; and pulmonary embolism. Pulmonary emergency conditions that can be diagnosed using point-of-care ultrasound include pneumothorax; pleural effusion; and interstitial syndrome. The extended focused assessment with sonography for trauma examination may of value in patients who are hypotensive in order to identify intra-abdominal haemorrhage, pneumothoraces and haemothoraces.
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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 | 0.007 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.014 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".