The impact of constructive feedback on training in gastrointestinal endoscopy using high-fidelity virtual-reality simulation: a randomised controlled trial
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
BACKGROUND: Recently, virtual reality computer simulators have been used to enhance traditional endoscopy teaching. Previous studies have demonstrated construct validity of these systems and transfer of virtual skills to the operating room. However, to date no simulator-training curricula have been designed and there is very little evidence on the impact of external feedback on acquisition of endoscopic skills. The aim of the present study was to assess the impact of external feedback on the learning curves on a VR colonoscopy simulator using inexperienced trainees. MATERIALS AND METHODS: 22 trainees, without colonoscopy experience were randomised to a group which received structured feedback provided by an experienced supervisor and a controlled group. All participants performed 15 repetitions of task 3 from the Introduction colonoscopy module of the Accu Touch Endoscopy simulator. Retention/transfer tests on simulator were performed 4-6 weeks after the last repetition. The proficiency levels were based on the performance of eight experienced colonoscopists. RESULTS: All subjects were able to complete the procedure on the simulator. There were no perforations in the feedback group versus seven in the non-feedback group. Subjects in the feedback group reached expert proficiency levels in percentage of mucosa visualised and time to reach the caecum significantly faster compared with the control group. None of the groups demonstrated significant degradation of performance in simulator retention/transfer tests. CONCLUSION: Concurrent feedback given by supervisor concur an advantage in acquisition of basic colonoscopy skills and achieving of proficiency level as compared to independent training.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 | 0.000 |
| 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