Point-of-care ultrasound: Usage and accuracy within a Canadian urology division
Bibliographic record
Abstract
INTRODUCTION: This research evaluates the utility and precision of point-of-care ultrasound (POCUS) in urology, inspired by recent affirmations of its feasibility and value.1,2 Our study provides valuable insights for urologists about POCUS's practical usage. METHODS: A prospective study assessed POCUS usage and accuracy in the University of Alberta's Division of Urology using data from April 4, 2022, to April 4, 2023. Data include POCUS indications, findings, and correlation with the final diagnosis/gold standard. Additionally, a qualitative survey was conducted among urologists and residents about POCUS's pros, cons, and barriers to integration. RESULTS: Thirty-three patients underwent POCUS examinations, mainly for suspected hydronephrosis (27%, n=9). Other indications included urinary retention, testicular mass, torsion, cryptorchidism, renal mass, extended focused assessment with sonography in trauma (eFAST ) exams, nephrostomy tube placement confirmation, and scrotal hematomas. POCUS findings matched the final diagnosis in most cases, showing 86% sensitivity, with an average exam time of 1-5 minutes. POCUS showed potential for suprapubic tube insertions. Residents (60%, n=20) were the most frequent users, followed by staff (33%, n=10), and students (6%, n=2). The surveyed urologists and residents expressed comfort with POCUS but cited time, cost, and practicality as barriers. CONCLUSIONS: POCUS proves accurate and beneficial in urology, particularly for hydronephrosis. Most findings align with the gold standard, and the average exam time is brief. Barriers include time and cost. Further research is necessary to evaluate cost-effectiveness and POCUS's impact on patient outcomes in routine urologic practice.
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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 | 0.003 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".