Implementation and Assessment of a Curriculum for Renal Point of Care Ultrasound (POCUS) Training
Bibliographic record
Abstract
Purpose: Renal ultrasound is a non-invasive method to assess for obstructive acute kidney injury (AKI). Point of care ultrasound (POCUS) has been shown to be a good screening tool for obstructive AKI, and with formal training, has high sensitivity and specificity. We aimed to evaluate the effectiveness and feasibility of integrating a novel renal POCUS curriculum into an existing two-week nephrology rotation for internal medicine residents. Methods: We enrolled internal medicine residents rotating on a two-week nephrology rotation between September 2022 and June 2023. Pre-recorded online lectures and a hands-on session on image acquisition were provided. Pre-and post-rotation confidence questionnaires and knowledge tests were collected. At the end of the rotation, participants were evaluated using a skills checklist. Evaluation for knowledge retention was assessed 6–12-months post-rotation with a post-survey and knowledge test. Results: Of the 16 residents that were enrolled, 12 residents completed pre- and post-rotation questionnaires and tests, and 15 residents completed the 6–12-month follow-up. The confidence level showed significant improvement post-test and at 6–12-month follow-up. Knowledge test scores showed a trend towards improvement that did not achieve statistical significance (pre- 6.0 [5.0-7.25], post- 6.5 [5.75-8.0], 6–12-months 7.0 [6.0-8.0] p=0.40). On the skills checklist, an average of 16.8 out of 18 steps were done correctly. Conclusion: Our study showed confidence improvement and a trend towards knowledge improvement after integrating a novel Renal POCUS curriculum into a nephrology rotation. Further iterative changes, such as deliberate practice, or practice with immediate feedback, should be considered.
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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 | 0.009 | 0.015 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".