Fecal Immunochemical Tests Compared with Guaiac Fecal Occult Blood Tests for Population-Based Colorectal Cancer Screening
Bibliographic record
Abstract
Colorectal cancer (CRC) is the second most common cause of cancer deaths in Canadian men and women - accounting for almost 12% of all cancer deaths. In Ontario, it is estimated that 8100 persons were diagnosed with CRC in 2011, and 3250 died from the disease. CRC incidence and mortality rates in Ontario are among the highest in the world. Screening offers the best opportunity to reduce this burden of disease. The present report describes the findings and recommendations of Cancer Care Ontario's Fecal Immunochemical Tests (FIT) Guidelines Expert Panel, which was convened in September 2010 by the Program in Evidence-Based Care. The purpose of the present guideline is to evaluate the existing evidence concerning FIT to inform the decision on how to replace the current guaiac fecal occult blood test with FIT in the Ontario ColonCancerCheck Program. Eleven articles were included in the present guideline, comprising two systematic reviews, five articles reporting on three randomized controlled trials, and reports of four other studies. Additionally, one laboratory study was obtained that reported on several parameters of FIT tests that helped to inform the present recommendation. The performance of FIT is superior to the standard guaiac fecal occult blood test in terms of screening participation rates and the detection of CRC and advanced adenoma. Given greater specimen instability with the use of FIT, a pilot study should be undertaken to determine how to implement the FIT in Ontario.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".