Effect of intravenous Buscopan on colonic distention during computed tomography colonography.
Bibliographic record
Abstract
OBJECTIVE: This study was designed to assess whether spasmolytic drugs are helpful in computed tomography colonography (CTC), as there is conflict in the literature. METHOD: We assessed retrospectively in a blinded fashion colonic distention in 149 individuals, one-half of whom had intravenous (IV) Buscopan during CTC. Colonic segments (n = 1788) were analyzed by 2 observers, and allocated to one of 4 grades of distention. We also recorded the presence and severity of diverticular disease. RESULTS: Buscopan increased the likelihood of optimal distention by an OR of 5 when considering individual colonic segments from ascending colon to sigmoid, with little effect on rectum or cecum. Considering the colon as a whole, the OR of optimal distention occurring throughout the entire colon was 7.9 times greater with Buscopan than without. In the sigmoid colon, Buscopan had a significantly greater impact on obtaining optimal distention in those with diverticulosis than in those without. CONCLUSION: Buscopan increases the probability of obtaining optimal distention during CTC, especially in the sigmoid colon in diverticular disease. Buscopan is likely to improve polyp conspicuity and patient comfort, and to reduce both the examination time and the interpretation time. We recommend routine use of Buscopan during CTC.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".