Multitarget stool DNA for colorectal cancer screening: A review and commentary on the United States Preventive Services Draft Guidelines
Bibliographic record
Abstract
Multitarget stool DNA (mt-sDNA) testing was approved for average risk colorectal cancer (CRC) screening by the United States Food and Drug Administration and thereafter reimbursed for use by the Medicare program (2014). The United States Preventive Services Task Force (USPSTF) October 2015 draft recommendation for CRC screening included mt-sDNA as an "alternative" screening test that "may be useful in select clinical circumstances", despite its very high sensitivity for early stage CRC. The evidence supporting mt-sDNA for routine screening use is robust. The clinical efficacy of mt-sDNA as measured by sensitivity, specificity, life-years gained (LYG), and CRC deaths averted is similar to or exceeds that of the other more specifically recommended screening options included in the draft document, especially those requiring annual testing adherence. In a population with primarily irregular screening participation, tests with the highest point sensitivity and reasonable specificity are more likely to favorably impact CRC related morbidity and mortality than those depending on annual adherence. This paper reviews the evidence supporting mt-sDNA for routine screening and demonstrates, using USPSTF's modeling data, that mt-sDNA at three-year intervals provides significant clinical net benefits and fewer complications per LYG than annual fecal immunochemical testing, high sensitivity guaiac based fecal occult blood testing and 10-year colonoscopy screening.
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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 | 0.007 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".