Addressing disorientation in endoscopy: A study on mental rotation difficulty and its impact on cognitive load measured by pupillary response
Bibliographic record
Abstract
One of the most notable challenges in endoscopic procedures is maintaining correct orientation. Mental rotation exercise (MRE) has been suggested as a potential aid for improving orientation. However, there is a lack of research on designing MREs with varying difficulty levels for training purposes. Furthermore, few studies provide solid evidence linking MRE difficulty levels with cognitive load measurements. This study aims to address this gap by investigating the correlation between the MRE difficulty levels and participants’ cognitive load, as measured by pupil dilation. We recruited 33 participants to perform MREs on a computer equipped with a screen-mounted eye-tracker. The test consisted of 15 MREs, with the first 10 relatively easy (traditional cube) and the next 5 more complex (invented molecule). The participants’ eye movements during MREs were recorded. The participants’ MRE scores and pupil dilation were obtained and compared between two MRE difficulty levels. The participants who performed traditional cube MREs achieved significantly better MRE scores (0.77 ± 0.11 vs. 0.58 ± 0.03, p < 0.001) and lower pupil dilation (0.27 ± 0.04 pixels vs. 0.47 ± 0.09 pixels, p < 0.001) than did those who performed the invented molecule MREs. Moreover, there were significant negative correlations ( r = −0.62, p = 0.015) between pupil dilation and MRE scores. The results revealed a significant negative correlation between MRE scores and pupil dilation. The more challenging MRE questions led to worse MRE scores but increased pupil dilation. The MRE difficulty levels can be evaluated not only by the degrees or dimensions with which the objects were rotated but also by the participants’ MRE scores and pupil dilation. The results of this study provide a basis for training orientation skills in endoscopy using MREs. By incorporating MREs with varying difficulty levels, customized training programs can be developed to enhance camera navigation in endoscopic and laparoscopic procedures.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".