Effectiveness of a Brief Point of Care Ultrasound Course at a National Nephrology Conference
Bibliographic record
Abstract
The rising demand for point of care ultrasound (POCUS) instruction during nephrology fellowship has been limited due to a shortage of trained faculty and courses designed specifically for nephrologists. A hands-on POCUS pre-course was organized during the April 2023 National Kidney Foundation (NKF) Spring Clinical Meeting to address this challenge. The course consisted of pre-recorded lectures and a 4-hour hands-on workshop guided by multidisciplinary POCUS experts. The anonymous post-course survey received responses from 25 out of 39 participants, yielding a 64.1% response rate. On a scale of 0-10, confidence levels for acquiring kidney images rose from 2.6 + 2.3 (mean + SD) pre-workshop to 7.8 + 1.5 post-workshop (p<0.001). Similarly, a remarkable improvement in confidence for acquiring lung and cardiac images was seen as scores increased from 1.8 + 2.4 to 7.7 + 1.5 (p<0.001) and from 1.5 + 2.2 to 7.2 + 1.3 (p<0.001), respectively. Additionally, respondents reported a substantial improvement in their confidence to interpret kidney, lung, and cardiac POCUS images, with scores increasing from 4.5 + 2.2 to 7.7 + 1.1 (p<0.001), 2.3 + 2.4 to 7.6 + 1.5 (p<0.001), and 2 + 2 to 7.3 + 1.5 (p<0.001), respectively. Barriers to implementing POCUS use at institutions included a perceived lack of trained faculty, limited protected time for faculty, and insufficient support from division leadership. The NKF POCUS pre-course successfully improved participants' confidence in acquiring and interpreting basic POCUS images.
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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 | 0.008 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".