PREDICATION FOR RELATIVE MOTION OF THE COLONOSCOPE IN COLONOSCOPY
Bibliographic record
Abstract
Colonoscopy is common procedure frequently carried out. It is not without its problems, which include looping formation. Looping formation prevents the tip of the colonoscope itself from advancing, thus further probing induces a risk of perforation, significant patient discomfort, and failure of colonoscopy. During colonoscopy, the manipulated colonoscope for intubation in the colon goes through the friction between the colonoscope and the colon. Due to major frictional force, the sigmoidal colon forms looping with the scope during intubation. The interactive frictional force between the colon and the colonoscope is highly complex because of frictional contact between two deformable objects. In this paper, contact force computation was formulated into a linear complementarity problem (LCP) by linearizing Signorini's problem, which was adapted into non-interpenetration with unilateral constraints. Frictional force was computed by the mechanical compliance of finite element method (FEM) models with the consideration of dynamic friction between the colonoscope and the intestinal wall. Furthermore, we presented a mathematical model of the elongation of the colon that predicts the motion of scope relative to the intestinal wall in colonoscopy.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".