Point-of-care-ultrasound for the assessment of post-renal transplant recipients
Bibliographic record
Abstract
INTRODUCTION: Postoperative imaging for deceased donor renal transplants is often delayed, as these surgeries occur after-hours. These delays can be critical in identifying immediate complications. To our knowledge, there are no formal training programs for point-of-care ultrasound (POCUS) in this setting; therefore, we aimed to develop and evaluate a feasible and practical POCUS curriculum for the assessment of a renal transplant graft. METHODS: Urology and nephrology transplant physicians completed a three-hour online course, followed by a five-hour hands-on seminar for sonographic scanning. Simulated patients with transplanted kidneys were used. Course material was developed with licensed ultrasound technologists based on Sonography Canada national competency profiles. Pre- and post-course surveys focused on user confidence, while pre- and post-course multiple-choice questionnaires assessed theoretical knowledge. RESULTS: Twelve participants were included, six of whom were urologists. Theoretical knowledge in POCUS improved significantly (p<0.001). Confidence in manipulation of ultrasound controls, Doppler imaging, and POCUS of the transplant kidney also improved (all p<0.001, d>2.0). Participants indicated an increased likelihood of POCUS use in clinical practice and that training should be integrated into a transplant fellowship. CONCLUSIONS: We introduced a novel and guideline-based POCUS curriculum that leveraged local ultrasound educators and found improved theoretical knowledge and skill confidence in our cohort of transplant physicians. This course will serve as the first step toward a validated competency-based training system for POCUS use in the immediate post-renal transplant setting, and likely will be incorporated into the training of the modern transplant physician.
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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 | 0.002 | 0.004 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".