Self-Stabilizing Colonic Capsule Endoscopy: Pilot Study of Acute Canine Models
Bibliographic record
Abstract
Video capsule endoscopy (VCE) is a noninvasive method for examining the gastrointestinal tract which has been successful in small intestine studies. Recently, VCE has been attempted in the colon. However, the capsule often tumbles in the wider colonic lumen, resulting in missed regions. Self-stabilizing VCE is a novel method to visualize the colon without tumbling. The aim of the present study was to comparatively quantify the effect of stabilization of a commercially available nonmodified capsule endoscope (CE) MiroCam and its modified self-stabilizing version in acute canine experiments. Two customized MiroCam CEs were reduced in volume at the nonimaging back-end to allow the attachment of a self-expanding, biocompatible stabilizing device. Four mongrel dogs underwent laparotomy and exteriorization of a 15-cm segment of the proximal descending colon. A single CE, either self-stabilizing or nonmodified was inserted through an incision into the lumen of the colon followed by pharmacologically induced colonic peristalsis. The inserted capsule was propelled distally through the colon and expelled naturally through the anus. Novel signal processing method was developed to quantify the video stabilization based on camera tracking a predetermined target point (locale). The average locale trajectory, the average radius movement of the locale, and the maximum rate of change of the locale for sequential images were significantly lower for the stabilized capsules compared to the nonstabilized ones . The feasibility of self-stabilized capsule endoscopy has been demonstrated in acute canine experiments.
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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 | 0.001 |
| 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 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".