A Simplified, Noninvasive Stool DNA Test for Colorectal Cancer Detection
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: As a noninvasive colorectal cancer (CRC) screening test, a multi-marker first generation stool DNA (sDNA V 1.0) test is superior to guaiac-based fecal occult blood tests. An improved sDNA assay (version 2), utilizing only two markers, hypermethylated vimentin gene (hV) and a two site DNA integrity assay (DY), demonstrated in a training set (phase 1a) an even higher sensitivity (88%) for CRC with a specificity of 82%. AIM: To validate in an independent set of patients (phase 1b) the sensitivity and specificity of sDNA version 2 for CRC. METHODS: Forty-two patients with CRC and 241 subjects with normal colonoscopy (NC) provided stool samples, to which they immediately added DNA stabilizing buffer, and mailed their specimen to the laboratory. DNA was purified using gel-based capture, and analyzed for hV and DY using methods identical to those previously published. RESULTS: Using the same cutpoints as the 1a training set (N = 162; 40 CRCs, 122 normals), hV demonstrated a higher and DY a slightly lower sensitivity, for a combined sensitivity of hV + DY of 86%. Optimal cutpoints based on the combined phase 1a + 1b dataset (N = 445; 82 CRCs, 363 normals) yielded a CRC sensitivity of 83%. The vast majority of cancers were detected regardless of tumor stage, tumor location, or patient age. Assay specificity in the phase 1b dataset for hV, DY, and hV + DY was 82%, 85%, and 73%, respectively, using the phase 1a cutpoints. Optimal cutpoints based on the combined phase 1a + 1b dataset yield a specificity of 82%. CONCLUSIONS: This study provides validation of a simplified, improved sDNA test that incorporates only two markers and that demonstrates high sensitivity (83%) and specificity (82%) for CRC. Test performance is highly reproducible in a large set of patients. The use of only two markers will make the test easier to perform, reduce the cost, and facilitate distribution to local laboratories.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| 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