Point‐of‐Care Ultrasound Education
Bibliographic record
Abstract
This article reviews the current technology, literature, teaching models, and methods associated with simulation-based point-of-care ultrasound training. Patient simulation appears particularly well suited for learning point-of-care ultrasound, which is a required core competency for emergency medicine and other specialties. Work hour limitations have reduced the opportunities for clinical practice, and simulation enables practicing a skill multiple times before it may be used on patients. Ultrasound simulators can be categorized into 2 groups: low and high fidelity. Low-fidelity simulators are usually static simulators, meaning that they have nonchanging anatomic examples for sonographic practice. Advantages are that the model may be reused over time, and some simulators can be homemade. High-fidelity simulators are usually high-tech and frequently consist of many computer-generated cases of virtual sonographic anatomy that can be scanned with a mock probe. This type of equipment is produced commercially and is more expensive. High-fidelity simulators provide students with an active and safe learning environment and make a reproducible standardized assessment of many different ultrasound cases possible. The advantages and disadvantages of using low- versus high-fidelity simulators are reviewed. An additional concept used in simulation-based ultrasound training is blended learning. Blended learning may include face-to-face or online learning often in combination with a learning management system. Increasingly, with simulation and Web-based learning technologies, tools are now available to medical educators for the standardization of both ultrasound skills training and competency assessment.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| 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) | 0.004 | 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".