Point-of-care ultrasonography for the diagnosis of testicular torsion: a practical resident curriculum
Bibliographic record
Abstract
Background: Prompt Doppler ultrasonography to aid in diagnosis is often key to managing testicular torsion, but there may be delays in access; a faster, more widely available alternative is point-of-care ultrasonography (POCUS). The purpose of this study was to develop and evaluate a scrotal POCUS curriculum for urology and emergency medicine residents. Methods: Content experts in urology, emergency medicine and diagnostic imaging collaborated in a modified Delphi method to design a practical didactic curriculum for scrotal POCUS for the identification of testicular torsion. Training included 3 online video teaching modules and a 1-hour hands-on teaching session with standardized adult patients. We evaluated participants' competency in scrotal POCUS using a validated scale. We assessed participants' knowledge, comfort and confidence in performing scrotal POCUS before and after the intervention and at 3 months. Results: Twenty-four urology (n = 12) and emergency medicine (n = 12) residents participated in the curriculum. After hands-on practice, 23 participants (96%) were deemed competent at scrotal POCUS. Pre-post testing showed significant improvement in knowledge (mean score 63% v. 80%, p < 0.001), comfort (mean Likert score 0.6 v. 3.6, p < 0.001) and confidence (mean Likert score 1.0 v. 2.1, p < 0.001) after the intervention. These effects were maintained at the 3-month assessment. Conclusion: The scrotal POCUS curriculum was effective and acceptable to both urology and emergency medicine residents. The findings suggest that scrotal POCUS can be learned effectively through a short hands-on session and didactic instruction.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".