Analyzing the curvature of the colon in different patient positions
Bibliographic record
Abstract
Purpose: Colonoscopy is a complex procedure with considerable variation among patients, requiring years of experience to become proficient. Understanding the curvature of colons could enable practitioners to be more effective. The purpose of this research is to develop methods to analyze the curvature of patients’ colons, and compare key segments of colons between supine and prone positions. Methods: The colon lumen in CT scans of ten patients are segmented. The following steps are automated by Python scripts in the 3D Slicer application: a set of center points along the colon are generated, and a curve is fit to these points. By identifying local maximums and local minimums in curvature, curves can be defined between two local curvature minimums. The angle of each curve is calculated over the distance of curves. Results: This automated process was used to identify and quantitatively analyze curves on the colon centerline in different patient positions. On average, there are 4.6 ± 3.8 more curves in supine position than prone. In the descending colon, there are more curves in the supine position, but curves in the prone position are larger. Conclusion: This process can quantify the curvature of colons, and can be adapted to consider other patient groups. Descriptive statistics indicate supine position has more curves in the descending colon, and prone has sharper curves in the descending colon. These preliminary results motivate further work with a larger sample size, which may reveal additional significant differences.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".