A155 VIDEO INTERVENTIONS TO IMPROVE SELF-ASSESSMENT ACCURACY IN GASTROINTESTINAL ENDOSCOPY: A SYSTEMATIC REVIEW
Bibliographic record
Abstract
Abstract Background Physicians generally have inaccurate self-assessment of their performance. One type of proposed intervention to improve this inaccuracy is the use of video-based feedback. The overall impact of this intervention on self-assessment accuracy in gastrointestinal endoscopy is unclear. Aims To systematically review current literature to determine if video-based interventions can effectively improve self-assessment accuracy in endoscopy. Methods We searched the following electronic databases from inception to 2019: Ovid MEDLINE; Ovid EMBASE, the Cochrane Register of Controlled Trials (CENTRAL); Education Resources Information Center (ERIC); Education Source on EBSCO; and Canadian Business Current Affairs EBM Reviews. Specifically, we searched for terms related to self-assessment, self-report, self-efficacy, video recording, and physician. Studies were included if they met the following criteria: physicians at any level of training and/or practice; studies that used an experimental design; compared self-rated assessments with external assessments of procedural skills in endoscopy; and at least one arm of the study involved a video-based intervention. Results Our search yielded 755 articles, of which 2 met all inclusion criteria. One study explored the use of three feedback interventions (practice only with no video; observation of their own video performance; observation of expert video performance) among general surgery residents performing flexible endoscopy, which found that only participants who watched expert video performances had improved accuracy of self-assessments. The other study investigated the use of three video interventions (video of own performance; video of expert performance; video of both own and expert performances) among novice endoscopists performing esophagoduodenoscopy (EGD), which found that the video of expert performance significantly improved self-assessment accuracy compared to the group with both videos. Conclusions The current data tentatively support the use of video review of expert performance to improve self-assessment accuracy in gastrointestinal endoscopy. A meta-analysis is planned to quantitatively assess the overall impact. Funding Agencies None
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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 | 0.008 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".