Do high-fidelity training models translate into better skill acquisition for an endourologist?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
PURPOSE OF REVIEW: Nowadays, accessibility to the operative room is becoming more limited for medical students and residents, principally due to decreasing operative time, increasing waiting list, ethical consideration and legal issue in case of any complications. Simulation models have gained in popularity and are now considered a major component in the training and skill development of medical students and residents before coming to the operative room. In this review, we summarized and discussed the relevant aspect of ureteroscopy training models and gave an overview of the advantage in skill acquisition while training with a high-fidelity model. RECENT FINDINGS: Currently, there is an increase in surgical programs trying to implement endourology training models into the curriculum. The training simulators that would allow the medical students and residents to rapidly reach an autonomous level are yet to be developed. Several ureteroscopy models have been described and validated; however, the transposition of skill acquisition into real-life surgery is not properly demonstrated. SUMMARY: Training reduces the learning curve for novice medical students or residents. However, further studies are still needed to better define the impact of skill acquisition in real life and its sustainability.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it