Poster Session 3: Education, Laparoscopy, Robotics, and Surgical Innovation
Bibliographic record
Abstract
Introduction: The 2024-25 AUA match cycle was the first since pre-COVID times to allow the return of in-person interviews.In prior years, applicants have progressively grown to favor virtual interviews over returning to an in-person format; however, a surprising lack of consensus still exists among applicants and program directors regarding the best and most equitable choice for interview formats going forward.Our aim was to assess urology residency applicants' preferences and perspectives regarding key issues in the urology match in this new era of mixed-format interviews.Methods: We surveyed applicants to our urology residency program from the 2024-2025 AUA match cycle.The primary aim was to assess applicant preferences and experiences by interview format: virtual vs. in-person.The secondary aim was to assess applicants' confidence in judging their "fit" with potential residency programs by interview format and based on various factors throughout the application cycle, grouped as pre-interview, day of interview, and post-interview.Additional secondary aims included investigating applicant costs/expenses, decisionmaking processes, factors most important for judging fit, and the role of hybrid interview formats.Results: Response rate was 45% (75/166).Applicants attended 13 interviews on average, seven in person and six virtual.Only 10% of invitations included a hybrid option, and just 9% offered any financial aid.Applicants strongly preferred in-person interviews (49.3%) or hybrid formats (40.0%) compared to virtual (10.7%).For judging fit, an even larger majority preferred in-person (76.0%) over virtual (4.0%), especially on the interview day itself.Applicants spent $4994 total (applications: $1913, interviews: $3081), averaging $410 per in-person interview.Nearly all applicants felt the costs and travel time of in-person interviewing were worth it (91% and 95%, respectively); however, 19% of applicants had to decline one or more in-person interviews due to costs (Figure 1).Conclusions: Urology applicants now largely favor in-person interviews over virtual, especially when judging "fit", and believe they are worth the increased costs and logistical challenges; however, financial constraints limited the access to interview opportunities for a substantial cohort.Future innovations in the urology match may explore broader implementation of hybrid interview formats as issues of applicant preferences and equity are balanced.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.274 | 0.048 |
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".