A45 IMPACT OF AN ERGONOMIC INTERVENTION ON SIMULATED COLONOSCOPY PERFORMANCE
Bibliographic record
Abstract
Endoscopists are at risk of musculoskeletal injury due to the repetitive strain of motions used during procedures. Additionally, appropriate ergonomic practice contributes to better performance in endoscopy. Despite this, gastroenterology trainees do not receive education about ergonomics. Furthermore, there is a lack of literature on effective methods to teach ergonomic principles and behaviours to trainees. To determine the effectiveness of an educational intervention for novice endoscopists designed to teach proper ergonomic principles to use in performing colonoscopy. Novice endoscopists (performed <50 previous colonoscopies) were enrolled in a simulation course in colonoscopy. The ergonomics teaching intervention consisted of the following: one hour didactic lecture; a video demonstrating ideal ergonomics during colonoscopy; and a self-reflection checklist on ergonomics in colonoscopy used after each simulated procedure. Participants were assessed at baseline (i.e. pre-test) and after the intervention (i.e. post-test) using the Rapid Upper Limb Assessment (RULA), an assessment tool of ergonomic behaviours. Higher scores on the RULA correspond to poorer ergonomic techniques. Sixteen residents completed the training intervention. The mean final scores based on the RULA for the pre-test was 6.31 (SD=0.70) and 5.31 (SD=1.58) for the post-test. All participants showed a significant decrease in their RULA scores (p<0.015). A teaching intervention for instruction of ergonomic principles in colonoscopy, involving a video, lecture, and self-reflection tool was associated with improved performance among novices. Further work should be performed to determine the most effective means of teaching proper ergonomic technique to novice endoscopists, and to determine if this training translates into improved clinical performance. 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".