Cost-Effectiveness of Novel Noninvasive Screening Tests for Colorectal Neoplasia
Bibliographic record
Abstract
BACKGROUND & AIMS: This study assessed the economic and health impact of colorectal cancer (CRC) screening programs for average-risk individuals aged 45 years and older. METHODS: A 10-year Markov model simulated disease progression, comparing multitarget stool RNA test (mt-sRNA, ColoSense), two mt-sDNA tests (Cologuard and Cologuard Plus), a blood-based test (cfDNA, Shield), and a fecal immunochemical test (FIT). Clinical inputs leveraged age-weighted sensitivity and specificity from independent studies. Outcomes were compared with a colonoscopy-based program and no screening. Model calibration and validation used previously reported Cancer Intervention Surveillance Modeling Network (CISNET) models. RESULTS: Among molecular tests, mt-sRNA detected the most advanced adenomas, referred the most individuals to surveillance, and prevented the highest number of CRC cases and deaths. At real-world adherence of 60%, mt-sRNA reduced CRC cases and deaths by 1% and 14% compared with FIT; by 21% and 19% compared with mt-sDNA; by 28% and 23% compared with mt-sDNA+; and by 80% and 86% compared with cfDNA. For all adherence levels, FIT ($25/test) was the most cost-effective strategy. For triennial molecular tests ($509/test), mt-sRNA was the most cost-effective strategy. Relative to the mt-sRNA program, the cost to prevent a CRC case was 30% (mt-sDNA), 45% (mt-sDNA+), and 642% (cfDNA) more expensive. Relative to the mt-sRNA program, the cost to prevent a CRC death was 30% (mt-sDNA), 41% (mt-sDNA+), and 1040% (cfDNA) more expensive. CONCLUSIONS: FIT was the most cost-effective strategy for preventing CRC cases and deaths. At real-world adherence of 60%, mt-sRNA demonstrated the greatest clinical benefit and was more cost-effective than other molecular strategies.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".