Simulation-Based Training of Non-Technical Skills in Colonoscopy: Protocol for a Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: Non-technical skills (NTS), such as communication and professionalism, contribute to the safe and effective completion of procedures. NTS training has previously been shown to improve surgical performance. Moreover, increases in NTS have been associated with improved clinical endoscopic performance. Despite this evidence, NTS training has not been tested as an intervention in endoscopy. OBJECTIVE: The aim of this study is to evaluate the effectiveness of a simulation-based training (SBT) curriculum of NTS on novice endoscopists' performance of clinical colonoscopy. METHODS: Novice endoscopists were randomized to 2 groups. The control group received 4 hours of interactive didactic sessions on colonoscopy theory and 6 hours of SBT. Hours 5 and 6 of the SBT were integrated scenarios, wherein participants interacted with a standardized patient and nurse, while performing a colonoscopy on the virtual reality (VR) simulator. The NTS (intervention) group received the same teaching sessions but the last hour was focused on NTS teaching. The NTS group also reviewed a checklist of tasks relevant to NTS concepts prior to each integrated scenario case and was provided with dedicated feedback on their NTS performance during the integrated scenario practice. All participants were assessed at baseline, immediately after training, and 4 to 6 weeks post-training. The primary outcome measure is colonoscopy-specific performance in the clinical setting. RESULTS: In total, 42 novice endoscopists completed the study. Data collection and analysis is ongoing. We anticipate completion of all assessments by August 2017. Data analysis, manuscript writing, and subsequent submission for publication is expected to be completed by December 2017. CONCLUSIONS: Results from this study may inform the implementation of NTS training into postgraduate gastrointestinal curricula. NTS curricula may improve attitudes towards patient safety and self-reflection among trainees. Moreover, enhanced NTS may lead to superior clinical performance and outcomes in colonoscopy. TRIAL REGISTRATION: Clinicaltrial.gov NCT02877420; https://www.clinicaltrials.gov/ct2/show/NCT02877420 (Archived by WebCite at http://www.webcitation.org/6rw94ubXX NCT02877420).
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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 | 0.034 | 0.039 |
| Meta-epidemiology (narrow) | 0.007 | 0.003 |
| Meta-epidemiology (broad) | 0.015 | 0.005 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.070 | 0.009 |
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".