Influence of video-based feedback on self-assessment accuracy of endoscopic skills: a randomized controlled trial
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Bibliographic record
Abstract
Abstract Background and study aims Novice endoscopists are inaccurate in self-assessment of procedures. One means of improving self-assessment accuracy is through video-based feedback. We aimed to determine the comparative effectiveness of three video-based interventions on novice endoscopists’ self-assessment accuracy of endoscopic competence. Materials and methods Novice endoscopists (performed < 20 previous procedures) were recruited. Participants completed a simulated esophagogastroduodenoscopy (EGD) on a virtual reality simulator. They were then randomized to one of three groups: self-video review (SVR), which involved watching a recorded video of their own performance; benchmark review (BVR), which involved watching a video of a simulated EGD completed by an expert; and self- and benchmark video (SBVR), which involved both videos. Participants then completed two additional simulated EGD cases. Self-assessments were conducted immediately after the first procedure, after the video intervention and after the additional two procedures. External assessments were conducted by two experienced endoscopists, who were blinded to participant identity and group assignment through video recordings. External and self-assessments were completed using the global rating scale component of the Gastrointestinal Endoscopy Competency Assessment Tool (GiECAT GRS). Results Fifty-one participants completed the study. The BVR group had significantly improved self-assessment accuracy in the short-term, compared to the SBVR group (P = .005). The SBVR group demonstrated significantly improved self-assessment accuracy over time (P = .016). There were no significant effects of group or of time for the SVR group. Conclusions Video-based interventions, particularly combined use of self- and benchmark video review, can improve accuracy of self-assessment of endoscopic competence among novices.
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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 | 0.002 |
| 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) | 0.002 | 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