Development and Evaluation of a Kidney Point-Of-Care Ultrasound (POCUS) Training Program for Nephrology Fellows: A Quality Improvement Study
Bibliographic record
Abstract
Background: Point of care ultrasound (POCUS) has become increasingly integrated into routine clinical care, though its adoption in nephrology remains limited. Objective: This pilot study evaluated a program to train nephrology fellows to perform POCUS to detect small kidney size and hydronephrosis. Design and Setting: We performed a quality improvement initiative at a single academic center (St. Michael's Hospital). Patients: 63 patients were included. Measurements: Pre- and post-workshop surveys assessed trainees' comfort level with kidney POCUS imaging. Patient satisfaction was also measured using a questionnaire. Time to kidney imaging, radiologic diagnosis and kidney POCUS diagnostic accuracy were also assessed. Methods: Nephrology fellows participated in two 1-hour workshops featuring didactic and hands-on training using POCUS machines, after which they scanned hospitalized patients. Results: Sixty-two native kidneys and 32 transplant kidneys were scanned. Patient surveys indicated high satisfaction with POCUS, with 71% preferring bedside ultrasound in future care. Trainee confidence with using POCUS improved post-workshop. Trainee-performed POCUS demonstrated a specificity of 1.00 (95% CI 0.94-1.00) and 0.96 (95% CI 0.80-1.00) for the detection of hydronephrosis in native and transplant kidneys, respectively. Nephrology trainees demonstrated a specificity of 0.75 (95% CI 0.53-0.90) [left native kidneys], 0.81 (95% CI 0.61-0.93) [right native kidneys] and 0.97 (95% CI 0.84-1.00) [transplant kidneys] for the detection of small kidney size. Limitations: The prevalence of hydronephrosis and small kidneys was too low in this pilot study to draw conclusions about sensitivity. Additionally, a majority of the kidneys underwent POCUS imaging after an ultrasound had been performed in the medical imaging department. Conclusions: Despite the increasing demand for POCUS training in nephrology, a significant gap persists in its clinical integration. Our study demonstrates that a structured workshop improves trainee confidence in kidney POCUS, with high patient satisfaction. Preliminary findings suggest that nephrology fellow-performed POCUS is feasible and promising, though further large-scale studies are needed to validate its clinical utility.
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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 | 0.040 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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 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".