A Colonoscopy Training Environment with Real-Time Pressure Monitoring
Bibliographic record
Abstract
Colonoscopy is the most commonly used procedure for screening and diagnosing colorectal cancers. The number of colonoscopy procedures performed each year is increasing as colon cancer currently ranks second among the leading cancers causing death worldwide. One of the significant factors resulting in incomplete colonoscopy procedures is pain and discomfort caused by the use of excessive force by the endoscopist. Learning the necessary skills for performing a successful procedure is technically challenging and requires substantial training. The present learning techniques primarily focus on completing the procedure rather than on controlling the applied pressure transmitted by the colonoscope to the colon wall. In this paper, we present a pressure-sensing sleeve that has been developed to provide real-time pressure monitoring during colonoscopy training on a simulator. This can be used along with movement tracking of the simulator colon and three-dimensional shape estimation of the colonoscope using electromagnetic tracking for training purposes. The sensing modalities complement each other and provide the trainee with real-time visual feedback on the section of the colonoscope transmitting the pressure to the colon during a simulated colonoscopy procedure. The pressure data, the colon movement data, and the position and the quality of advancement of the scope through the colon are used to objectively evaluate the trainee’s performance using the metrics described in this paper. Such a training environment can help in providing the necessary skills to trainees while keeping the transmitted pressure within safe limits to minimize patient discomfort and maximize safety.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".