Bibliographic record
Abstract
U rology was the second specialty cohort to adopt competency-based medical education (CBME) in 2018.The consensus of the Urology Specialty Committee leading up to implementation was that we were a relatively small, innovative, and flexible specialty that should jump on board early and have an influence on the educational frameworks.This approach was perceived to be superior to having pre-established templates imposed upon us down the road.The creation of the Urology CBME framework was laborious and contentious, with many stakeholders from around the country contributing.The resulting document suites and entrusted professional activities (EPAs) were imperfect as we rolled out the initiative.The results of the survey published in this month's issue of CUAJ clearly reflect those imperfections. 1 For example, most respondents are dissatisfied with CBD (69%) owing to a host of factors that include evaluation fatigue and a perceived lack of improvement in patient care and safety.There has been no apparent change in the pathway to graduation or a de-emphasis on time-based learning and examinations (these were the main selling points of CBD after all!).In addition, the majority of faculty feel that the EPAs don't reflect actual clinical practice.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.024 | 0.079 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.028 |
| Scholarly communication | 0.014 | 0.015 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.018 | 0.003 |
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".