Evaluation of Optimal Timing of Expert Feedback in a Simulated Flexible Ureteroscopy Course
Bibliographic record
Abstract
Introduction: Simulation-based training (SBT) has become an increasingly popular modality to train novice surgical residents in the face of rapidly increasing innovative surgical techniques across all surgical disciplines. Recent studies have already demonstrated SBT to be effective in helping overcome the learning curve associated with new surgical techniques, especially in junior residents and endoscopic procedures. In addition, it is known that trainees benefit significantly from expert feedback; however, there is a paucity of data looking into the optimal timing of this feedback during SBT. To address this knowledge deficit, an SBT curriculum was developed for junior urology residents to assess optimal timing of feedback during SBT for flexible ureteroscopy (fURS). Materials and Methods: The SBT course consisted of a pretraining assessment, three independent practice sessions, and a post-training assessment, with residents receiving expert feedback right after their pretraining assessment (early feedback [EF]) or after their final independent training session (late feedback [LF]). Results: Fifteen trainees with similar baseline fURS experience and precourse fURS task performance score participated in the study. There was a significant difference between the pre- and post-task completion times overall (15.2 minutes vs 9.1 minutes, p < 0.001), with no difference between the early or LF groups (p = 0.884). The mean performance scores improved for both groups (18.2 vs 24.2, p < 0.001) with the EF group having a more statistically significant improvement in performance scores than the LF group (p = 0.05), and most (73%) of residents preferred EF. Conclusions: This study demonstrates that an SBT curriculum for fURS is effective for technical skills development among junior trainees, and that EF resulted in marginally better overall scores and was preferred by residents.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".