Training strategies for point of care ultrasound in the ICU
Bibliographic record
Abstract
PURPOSE OF REVIEW: Ultrasound in critical care medicine (CCUS) is a relatively young tool that has been evolving rapidly as skillsets, applications and technology continue to progress. Although ultrasound is identified as a core competency in intensive care unit (ICU) training, there remains significant variability and inconsistencies in the delivery of ultrasound training. The goal of this narrative review is to explore areas of consensus and highlight areas where consensus is lacking to bring attention to future directions of ultrasound training in critical care medicine. RECENT FINDINGS: There exists considerable variation in competencies identified as basic for CCUS. Recent efforts by the European Society of Intensive Care Medicine serve as the most up to date iteration however implementation is still limited by regional expertise and practice patterns. Major barriers to ultrasound training in the ICU include a lack of available experts for bedside teaching and a lack of familiarity with new technology. SUMMARY: Though international uptake of CCUS has made many gains in the past 20 years, further adoption of technology will be required to overcome the traditional barriers of CCUS training. Although the availability and time constraints of experts will remain a limitation even with wireless capabilities, the ability to expand beyond the physical constraints of an ultrasound machine will vastly benefit efforts to standardize training and improve access to knowledge.
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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 | 0.003 | 0.010 |
| 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 | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".