Analysis of and mathematical model insight into loop formation in colonoscopy
Bibliographic record
Abstract
The colonoscope is an important tool in the diagnosis and management of diseases of the colon; yet its design has not changed appreciably since it was first introduced to clinical practice 40 years ago. One of the ongoing challenges with this device is that the natural shape of the colon predisposes to loop formation by the scope during the examination. The result of this looping is that further insertion of the scope results in a larger loop size without any advancement of the tip of the scope. Looping thus causes pain in the patient, risks perforation of the colon, and results in incomplete examinations. In this article, loop formation is analyzed in terms of frictional force state and Kirchhoff's slender rod model in order to better understand the generic principle of loop formation. Next, a mathematical model of deformation of the colon with respect to external manipulation involving a number of variables involved in loop formation is constructed. Finally, a model of the motion of the scope relative to the colon when looping occurs is presented. The model has clinical significance for prediction of advancement of the tip of the scope when looping occurs. The mathematical model was then validated and verified using data available from the literature. Our models are an important starting point in the development of a novel device to overcome loop formation and result in increased patient comfort and an improved completion rate for colonoscopy 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".