Simulation Research in Gastrointestinal and Urologic Care—Challenges and Opportunities
Bibliographic record
Abstract
A workshop on ''Simulation Research in Gastrointestinal and Urologic Care: Challenges and Opportunities'' was held at the National Institutes of Health in June 2016. The purpose of the workshop was to examine the extent to which simulation approaches have been used by skilled proceduralists (not trainees) caring for patients with gastrointestinal and urologic diseases. The current status of research findings in the use and effectiveness of simulation applications was reviewed, and numerous knowledge gaps and research needs were identified by the faculty and the attendees. The paradigm of ''deliberate practice,'' rather than mere repetition, and the value of coaching by experts was stressed by those who have adopted simulation in music and sports. Models that are most useful for the adoption of simulation by expert clinicians have yet to be fully validated. Initial studies on the impact of simulation on safety and error reduction have demonstrated its value in the training domain, but the role of simulation as a strategy for increased procedural safety remains uncertain in the world of the expert practitioner. Although the basic requirements for experienced physicians to acquire new skills have been explored, the widespread availability of such resources is an unrealized goal, and there is a need for well-designed outcome studies to establish the role of simulation in improving the quality of health care.
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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 | 0.203 | 0.111 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.011 |
| Scholarly communication | 0.010 | 0.014 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.007 | 0.014 |
| 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".