Teaching and evaluation of basic urodynamic skills in urology residency programs: Randomized controlled study
Bibliographic record
Abstract
AIMS: Considering the growing role of urodynamic studies (UDS) in urology, we aimed to determine the most effective teaching method with objective evaluation for urodynamic skills, to improve training and patient care. METHODS: Urology residents (n = 20) post-graduate years 3-5 were randomized to receive either a UDS video training module or a standard UDS teaching document one week prior to an objective structured clinical examination (OSCE). The OSCE was a validated visual recognition exam with interpretation of 12 UDS tracing scenarios. Participants rated their proficiency to interpret UDS tracings before doing the OSCE. Total interpretation score was determined by the accuracy of their response to each question ranging from 0 to 2. RESULTS: The mean total interpretation score was 13.3 of 24 (55%). The video group achieved significantly higher interpretation scores (15.1 ± 2.08 vs 11.4 ± 2.41, P = 0.0017), and cumulative certainty scores (P = 0.0341). Overall interpretation scores significantly correlated with self-reported proficiency scores prior to the exam (r = 0.502, P < 0.05), and total certainty scores (r = 0.531, P < 0.05). CONCLUSIONS: Reviewing a UDS video training module resulted in significantly better scores on objective assessment of urology residents' UDS interpretation skills when compared with a standard teaching document. These findings must be interpreted with caution in light of sample size and short knowledge retention required for the assessment within a week. Therefore, using a UDS video training module could be more effective review tool for urology residents. These findings highlight the need to incorporate multimedia teaching into urology training curriculum.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".