Evaluierung des GeSRU-Steps-Lehrvideokonzepts (German Society of Residents in Urology e. V.)
Bibliographic record
Abstract
BACKGROUND: Surgical educational videos represent a contemporary, multimedia supplement to surgical education and training. The German Society of Residents in Urology e. V. (GeSRU) developed an educational video platform (steps.GeSRU.de) with free, quality-assured educational videos for urologists, especially for residents. OBJECTIVES: The purpose of this study was to evaluate the GeSRU Steps teaching videos. MATERIALS AND METHODS: Prospectively, 29 GeSRU Steps training videos were made available (03/2019-05/2023) via amboss.com, and an online questionnaire was inserted following the videos. This comprised 12 items on medical, technical, and didactic quality, usefulness for own knowledge acquisition, and sociodemographic data of respondents. Aspects of video quality were assessed with the Acceptability E‑scale and the Global Quality Score. RESULTS: During the survey period, the GeSRU Steps videos implemented on the amboss.com website were viewed 49,698 times. A total of 474 questionnaires were answered (rate 0.25%). The collective of respondents consisted of 419 (88%) students, 47 (10%) physicians in training, and 5 (1%) specialists; 351 (74%) were female, 107 (23%) were male, and 4 (1%) were diverse. Each educational video was rated a median of 10 times (range 5-65). The six questions of the Acceptability E‑scale and the Global Quality Score were rated good and very good (81.6-95.8%), respectively. CONCLUSIONS: GeSRU teaching videos achieved a very good rating with high user satisfaction. By specific promotion of these teaching videos, which are quality-assured through supervision, the portfolio of surgical videos available at a low threshold can be expanded and can serve as a contemporary education tool.
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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 | 0.010 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".