Evaluation of an Electronic Platform for Problem Based Learning for Subspecialty Fellows
Bibliographic record
Abstract
INTRODUCTION: Increasing demands on the time of trainees may warrant new self-directed, concise methods of problem based learning. To address these issues in urological oncology CBULP was designed to provide a concise electronic format that could be readily accessed when the fellow was rested and ready to learn. We evaluated the perceived usefulness of this program. METHODS: Subspecialists from 2 academic urology programs and an educational professional wrote 42 clinical scenarios about various renal and adrenal malignancies, and generated concise learning points. These cases were mailed to various urological oncology fellowships in the United States and Canada. An 18-question survey was delivered electronically 8 weeks later. Responses were recorded anonymously via survey software. RESULTS: Of 36 fellows 30 (83%) responded. Of the respondents 74% completed at least 5 cases and the majority completed more than 10. Of the respondents 93% thought that the cases had the appropriate amount of detail and covered core concepts related to renal/adrenal tumors. No respondent required more than 20 minutes to finish any case. Of the respondents 93% and 100% indicated that the cases effectively illustrated the basic principles of the disease process, and the fundamentals of evaluation and management, respectively. Overall 97% of respondents thought that CBULP could be an effective learning resource for fellows. CONCLUSIONS: An electronic case based method of learning appears to be a useful tool for subspecialty fellows. It may be a worthwhile self-directed supplement to traditional educational resources.
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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 | 0.012 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".