NS-AUA 2023 Annual Meeting Abstracts – Education, Laparoscopy, Robotics, Surgical Innovation
Bibliographic record
Abstract
Introduction: Office urology practice forms a significant portion of any urological program.Medical schools' curricula may contain exposure to hospital surgical operating room but little of office practice procedures.As more procedures become office-based, learner's preparation in techniques and competency is imperative.Between May 25, 2022, and September 30, 2022, a module was developed and implemented for an office urology practice curriculum based on Kolb's experiential learning model for possible adaption to a training program.Method: The module was developed based on literature review and needs assessment.Volunteer learners were recruited.Consent to participate and confidentiality agreement were obtained.Various conditions of the lower urinary tract were categorized; subjects identified and consented.Participants were scheduled to a learning experience and cycle through multiple times The cycle of experience as follows: concrete experience-observation by learner of direct care by faculty; reflective observation-faculty-facilitated reflection, learning contracts, and feedback; abstract conceptualization: discuss other diagnosis and management strategies based on experience; and active experimentation/hands-on practice with real patient scenarios.Feedback from volunteer learners and patient participants was obtained orally and/or online.Results: There were 4 volunteer learner participants (two undergrad university and 2 nurses).Patient encounter scenarios were BPH, hematuria, incontinence, dysuria, nocturia, and overactive bladder.Learning experience included: performing and interpreting uroflows, ultrasound, and digital rectal exams; obtaining consent and informed consent process; IPSS and urinary diary.This process provided guidance to learners to acquire skills useful for future career and for building facultylearner relationships.Learners were given the opportunity to make their choices and faculty encouraged them to create their learning objectives, identify resources, and devise strategies to achieve their learning objectives.Limitations included the fact that this was a pilot program, with few participants, as well as potential faculty bias and limited ultrasound use.Conclusions: Kolb's experiential learning theory, despite limitations, is useful for a learner module in office urology curriculum.It is recommended for trial in both well-established and newer urology programs.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.494 | 0.277 |
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".