A Novel, Flexible, Full-Length, Pressure-Sensing Sleeve for Colonoscopes
Bibliographic record
Abstract
Colonoscopy is the gold standard for screening and diagnosing colorectal cancers. One of the important factors resulting in an incomplete colonoscopy procedure is direct trauma to the colon due to large forces applied during the insertion of a colonoscope which causes significant pain and discomfort to the patient. Acquiring the necessary skills for performing a successful procedure is technically challenging and requires substantial training. The current learning techniques primarily focus on completing the procedure without realizing the intensity of the pressure transmitted from the colonoscope to the colon wall. In this article, a novel pressure-sensing sleeve is presented that can be used to sensorize a commercially available adult colonoscope (Olympus CF-H180AL). The sleeve can be used in an interactive simulator-based training environment capable of providing real-time feedback on the pressure transmitted by the scope to the simulator colon. The sensor was designed using flexible printed circuit boards (PCBs) and using the piezoresistive behavior of carbon-filled polymers (CFPs). Different CFP materials and measurement circuits were evaluated to select the ideal combination for reliable measurement of pressure values up to 34 kPa, at which perforations of the colon could occur. Care was also taken that the overall increase in the diameter of the colonoscope with the sleeve was not more than 1 mm. The sleeve was designed to cover an axial length of 1150 mm, and the scope remained sufficiently flexible for use with negligible sensitivity to the bending of the scope when no pressure was applied to the sleeve.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".