Early Use of Magnetic Endoscopic Imaging by Novice Colonoscopists: Improved Performance without Increase in Workload
Bibliographic record
Abstract
BACKGROUND: Magnetic endoscopic imaging represents a recent advance in colonoscopy training. This technique provides adjunct information to the endoscopist, specifically with regard to colonoscope loop formation. OBJECTIVE: To examine the effect of a magnetic endoscopic imager on novice performance and workload in colonoscopy. METHODS: Twenty complete novices received an introductory teaching session followed by the completion of two procedures on a colonoscopy model. One-half of the participants performed their first procedure with the imager, and the second procedure without, while the other one-half were trained with the inverse sequence. Two main outcome measures were recorded: distance achieved and total workload as measured by the National Aeronautics and Space Administration task load index tool. RESULTS: A significant improvement was noted between the first and second colonoscopies, with the best performance recorded for participants who performed their first procedure with the imager, and their second without. The imager did not significantly change the total workload. DISCUSSION: The study participants paid attention to the magnetic endoscopic imager; however, this did not translate into a measurable increase in novice workload. A delayed learning benefit was conferred to the group exposed to the imager on their first colonoscopy, suggesting that, even at an early training stage, the additional imager information entered working memory and was processed in a useful fashion. The introductory teaching strategy used in the present study was successful as judged by the overall distance achieved and performance improvement seen in all study participants.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".