OVERVIEW OF UPCOMING ADVANCES IN COLONOSCOPY
Bibliographic record
Abstract
Although colonoscopy is a very commonly carried out procedure, it is not without its problems, including a risk of perforation and significant patient discomfort, especially associated with looping formation. Furthermore, looping formation may prevent a complete colonoscopy from being carried out in certain patients. The conventional colonoscope has not changed very much since its original introduction. We review promising technologies that are being promoted as a way to address the problems with current colonoscopy. There are some methods to prevent looping formation, including overtube, variable stiffness, computer-guided scopes, Aer-O-Scope, magnetic endoscopic imaging and the capsule endoscope. In recent years, with the progress of microelectromechanical and microelectronic technologies, many biomedical and robotic researchers are developing autonomous endoscopes with miniaturization of size and integration functionality that represent state of the art of the micro-robotic endoscope. The initial results by using aforementioned methods seem promising; however, there are some conflicting reports of clinical trials with the overtube colonoscope, the computer-guided scope and the variable stiffness colonoscope. There are also some limitations in the use of the Aer-o-scope and the capsule endoscope. The autonomous endoscope is based on a self-propelling property that is able to avoid looping completely. This novel technology could potentially become the next generation endoscope; however, there are still critical techniques to be approached in order to develop the effective and efficient novel endoscope.
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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 | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.005 |
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".