The Benefits of Computed Tomographic Colonography in Reducing a Long Colonoscopy Waiting List
Bibliographic record
Abstract
PURPOSE: The Radiology Department, Royal Jubilee Hospital, Victoria, BC, with the support of gastroenterologists and surgeons, was awarded a BC Innovation fund to run a pilot project of computed tomographic colonography to reduce an unacceptably long 2-year colonoscopy waiting list. Funds were approved in April 2007 for a 1-year project, which was completed on March 31, 2008. METHODS: This article describes the challenges of delivering a high-volume computed tomographic colonography program at a busy community hospital, with discussion of the results for the 2,005 patients who were examined. RESULTS: Colonoscopy was avoided in 1,462 patients whose computed tomographic studies showed no significant lesions. In the remainder of patients, only lesions larger than 5 mm were reported, with a total of 508 lesions identified in 433 patients. There were 57 cancers of which 52 were reported as either definite or possible cancers, whereas 5 were not seen on initial scans. Some of the patients with cancer had been on the colonoscopy waiting list for 2 years. In addition, there were 461 patients with significant extracolonic findings, including 84 who required urgent or semi-urgent further management for previously unsuspected conditions, such as pneumonia, aneurysms larger than 5 cm, and a range of solid renal, hepatic, and pancreatic masses. There were no procedural complications from the computed tomographic colon studies. CONCLUSIONS: We have shown that it is feasible to run a high volume CTC service in a general hospital given hospital support and funding. The benefits in this group of over 2000 patients included avoidance of colonoscopy in over 70% of patients, detection of significant polyps or cancer in approximately 20% of patients, and identification of clinically important conditions in 7%-18% depending on the definition used. The estimated costs including capital, operating, and professional fees were in the range of $400.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".