COST-EFFECTIVENESS OF COMPUTED TOMOGRAPHY COLONOGRAPHY IN COLORECTAL CANCER SCREENING: A SYSTEMATIC REVIEW
Bibliographic record
Abstract
OBJECTIVES: The European Code Against Cancer recommends individuals aged ≥ 50 should participate in colorectal cancer screening. CT-colonography (CTC) is one of several screening tests available. We systematically reviewed evidence on, and identified key factors influencing, cost-effectiveness of CTC screening. METHODS: PubMed, Medline, and the Cochrane library were searched for cost-effectiveness or cost-utility analyses of CTC-based screening, published in English, January 1999 to July 2010. Data was abstracted on setting, model type and horizon, screening scenario(s), comparator(s), participants, uptake, CTC performance and cost, effectiveness, ICERs, and whether extra-colonic findings and medical complications were considered. RESULTS: Sixteen studies were identified from the United States (n = 11), Canada (n = 2), and France, Italy, and the United Kingdom (1 each). Markov state-transition (n = 14) or microsimulation (n = 2) models were used. Eleven considered direct medical costs only; five included indirect costs. Fourteen compared CTC with no screening; fourteen compared CTC with colonoscopy-based screening; fewer compared CTC with sigmoidoscopy (8) or fecal tests (4). Outcomes assessed were life-years gained/saved (13), QALYs (2), or both (1). Three considered extra-colonic findings; seven considered complications. CTC appeared cost-effective versus no screening and, in general, flexible sigmoidoscopy and fecal occult blood testing. Results were mixed comparing CTC to colonoscopy. Parameters most influencing cost-effectiveness included: CTC costs, screening uptake, threshold for polyp referral, and extra-colonic findings. CONCLUSION: Evidence on cost-effectiveness of CTC screening is heterogeneous, due largely to between-study differences in comparators and parameter values. Future studies should: compare CTC with currently favored tests, especially fecal immunochemical tests; consider extra-colonic findings; and conduct comprehensive sensitivity analyses.
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 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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".