Comparison of One versus Two Fecal Immunochemical Tests in the Detection of Colorectal Neoplasia in a Population-Based Colorectal Cancer Screening Program
Bibliographic record
Abstract
Objective. To determine the positive predictive value (PPV) of two versus one abnormal FIT in the detection of colorectal neoplasia in a Canadian population. Methods. Three communities enrolled in a colorectal cancer (CRC) screening pilot program from 01/2009 to 04/2013 using 2 FITs. Data collected included demographics, colonoscopy, pathology, and FIT results. Participants completed both FITs and had one positive FIT and colonoscopy. PPV of one versus two abnormal FITs was calculated using a weighted-generalized score statistic. A two-sided 5% significance level was used. Results. 1576 of 17,031 average-risk participants, 50–75 years old, had a positive FIT. Colonoscopy revealed 58 (3.7%) cancers, 419 (31.6%) high-risk polyps, and 374 (23.7%) low-risk polyps as the most significant lesion. PPV of one versus two positive FITs for cancer, high-risk polyps, and any neoplasia were 1% versus 8%, 20% versus 40%, and 48% versus 67%, respectively (<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M1"><mml:mrow><mml:mi>p</mml:mi></mml:mrow></mml:math>value < 0.0001). When the first FIT was negative, the second positive FIT detected 7 CRCs and 98 high-risk polyps. Conclusions. PPV of two positive FITs is superior to one positive FIT for CRC and high-risk polyps. The added value of the second FIT was 12% of total CRCs and 23% of total high-risk polyps.
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How this classification was reachedexpand
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) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".