The cost-effectiveness of screening for colorectal cancer
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Published decision analyses show that screening for colorectal cancer is cost-effective. However, because of the number of tests available, the optimal screening strategy in Canada is unknown. We estimated the incremental cost-effectiveness of 10 strategies for colorectal cancer screening, as well as no screening, incorporating quality of life, noncompliance and data on the costs and benefits of chemotherapy. METHODS: We used a probabilistic Markov model to estimate the costs and quality-adjusted life expectancy of 50-year-old average-risk Canadians without screening and with screening by each test. We populated the model with data from the published literature. We calculated costs from the perspective of a third-party payer, with inflation to 2007 Canadian dollars. RESULTS: Of the 10 strategies considered, we focused on three tests currently being used for population screening in some Canadian provinces: low-sensitivity guaiac fecal occult blood test, performed annually; fecal immunochemical test, performed annually; and colonoscopy, performed every 10 years. These strategies reduced the incidence of colorectal cancer by 44%, 65% and 81%, and mortality by 55%, 74% and 83%, respectively, compared with no screening. These strategies generated incremental cost-effectiveness ratios of $9159, $611 and $6133 per quality-adjusted life year, respectively. The findings were robust to probabilistic sensitivity analysis. Colonoscopy every 10 years yielded the greatest net health benefit. INTERPRETATION: Screening for colorectal cancer is cost-effective over conventional levels of willingness to pay. Annual high-sensitivity fecal occult blood testing, such as a fecal immunochemical test, or colonoscopy every 10 years offer the best value for the money in Canada.
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.
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 | 0.003 | 0.004 |
| 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 | 0.001 |
| 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 it