The Role of Computed Tomographic Colonography in Colorectal Cancer Screening
Bibliographic record
Abstract
Objective: We conducted a literature review to identify the current state of knowledge regarding the optimal clinical use of computed tomographic colonography (CTC) in Canada, based on accuracy, patient safety, and costeffectiveness. Methods: Articles were retrieved from PubMed and the Cochrane Library. Retrieved studies were included based on relevance and appropriateness as determined by reviewing titles and abstracts. Studies were excluded if they were duplicated, grey literature, or non-peer-reviewed. Of the studies remaining after exclusions, reference lists were scanned to obtain further relevant articles. Results: The literature reports comparable accuracy for detecting cancers and large polyps, yet CTC is less sensitive than colonoscopy for detecting small polyps. Most would agree that CTC is safer than colonoscopy, yet it is not without risk or adverse events. Lastly, although the true costs of CTC vs. colonoscopy are complex, the literature consistently demonstrates that CRC screening with CTC is less cost-effective than screening with colonoscopy. Conclusion: Unless there are modifications to CTC that improve cost-effectiveness and/or accuracy, the future of CRC screening in Canada will remain reliant on colonoscopy. CTC is beneficial as an alternative to colonoscopy, but should remain available for selected indications. CTC has value, however, it has fallen short of initial expectations.
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 | 0.012 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.014 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".