Integrating Point of Care Ultrasound into Nephrology Fellowship Training: Insights from a Pilot Program
Bibliographic record
Abstract
In nephrology, point-of-care ultrasound (POCUS) has multiple applications including the rapid evaluation of acute kidney injury, enhancing the initial evaluation of chronic kidney disease, direct evaluation of vascular access, and improved fluid balance management in acute and chronic settings [1, 2]. Recently, the role of POCUS has been formally acknowledged by the American College of Physicians and curricula specific to nephrology have been proposed [3, 4]. However, the integration of a novel clinical skill into a field comes with its unique set of challenges. Above all, most nephrologists in leadership roles within fellowship training programs lack POCUS experience, which represent a significant barrier for adequate exposure and teaching. Although educational curriculum centered on nephrology have been proposed, the optimal model to ensure adequate POCUS exposure considering the scarcity of expertise among educators is not known.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".