A158 LACK OF DIFFERENCE OF COLONIC CURVATURE IN SUPINE VERSUS PRONE PATIENT POSITIONS, IN NORMAL AND HIGH BMI INDIVIDUALS, AS ASSESSED BY QUANTITATIVE ASSESSMENT OF COMPUTED TOMOGRAPHY COLONOGRAPHY
Bibliographic record
Abstract
Abstract Background Dynamic positional changes during colonoscopy are commonly used in clinical practice, in particular moving from side to side. It has been shown to improve both adenoma detection rates as well as cecal intubation times. However, perhaps due to an additional level of inconvenience, there have been few studies comparing the anatomy and changes in colonic curvature when patients are in the prone position, which may help to prevent anterior bowing of the scope, particularly in patients with high body mass index (BMI). Aims To compare both the number of colonic curves and degree of change in curves with patients in supine versus prone positioning during computed tomography colonography (CTC). Methods 75 CTC studies, obtained between January and April 2017 at Hotel Dieu Hospital in Kingston, Ontario, were screened and included based on image quality and adequacy of distention. Per standardized protocol, all patients undergoing CTC are imaged both in supine and prone positioning. Using an automated computer algorithm process developed for this study, curves were identified and measured via centerline points placed digitally through the colonic lumen, and compared between supine and prone patient positioning. Results 75 colonographies were examined. The mean age was 68 years and 37/75 were male. BMI data was available for 56 patients, with mean BMI 29.4 (SD 5.7). There were no significant differences in total mean degrees of curvature between supine and prone positions [75.3 (SD 13.5) vs. 77.3 (SD 15.3), p=0.07], nor a significantly higher total number of curves >100 degrees [4.0 (SD 2.0) vs. 4.5 (SD 2.3), p=0.14]. No significant correlation was seen between BMI and change in position (correlation factor 0.2, p=0.13). Conclusions No significant differences were found between the two positions during CT colonography. This certainly calls into question the strategy of starting in prone position, even in higher BMI patients. However, CT colonography doesn’t account for changes that can occur during colonoscopy, as the scope itself can dynamically affect angulations within the colon. Funding Agencies None
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".