Comparative Effectiveness and Cost-Effectiveness Analysis of a Urine Test versus Alternative Colorectal Cancer Screening Strategies
Bibliographic record
Abstract
Introduction: Despite recent declines in both incidence and mortality through screening programs, colorectal cancer (CRC) is still the second most common cause of cancer death in Canada. Current fecal-based tests recommended in the CRC screening programs are hampered by poor adherence as well as low sensitivity for colonic adenomatous polyps, the pre-cursor to CRC. A novel metabolomic-based urine test has been developed, to accurately identify persons with colonic adenomatous polyps. This study compares the cost effectiveness of the urine test with currently recommended CRC screening strategies. Methods: Effectiveness and cost-effectiveness analysis were performed using a Markov model calibrated against cancer statistics data. Persons at average CRC risk within the general Canadian population were modeled. Operating characteristics of the strategies were derived from the literature and local costs of screening and treatment were used. Screening strategies included colonoscopy every 10 years, annual fecal guaiac test (FGT), annual fecal immunochemical test (FIT) and a metabolomics-based urine test (PolypDxTM) every 2 years. The adherence rate of each screening strategy was derived from literature. The quality adjusted life years (QALY) gained, costs, and incremental cost-effectiveness ratios (ICERs) for each strategy were estimated and compared with no screening. Results: Compared with no screening, a metabolomics-based urine screening strategy reduced CRC mortality by 41% and gained 0.13 life-years per person at $46,783/life-year gained in the base case. A FGT screening strategy reduced CRC mortality by15% and gained 0.04 life-years per person at $29,568/ life-year gained. A FIT screening strategy reduced CRC mortality by 36% and gained 0.11 life-years per person at $31,008/life-year. A colonoscopy screening screening strategy reduced CRC mortality by 25% and gained 0.08 life-years per person at $51,616/life-year gained. Conclusion: Despite the higher cost, the metabolomics-based urine screening strategy was the most effective method, compared with conventional screening strategies, for reducing incidence and mortality of CRC and QALY gained. The urine-based screening program had the second highest ICER at $46,783 and was still considered to be a cost-effective strategy.
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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 | 0.011 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.007 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".