SCREENING FOR COLORECTAL CANCER USING THE FECAL OCCULT BLOOD TEST: AN ACTUARIAL ASSESSMENT OF THE IMPACT OF A POPULATION-BASED SCREENING PROGRAM IN CANADA
Bibliographic record
Abstract
OBJECTIVES: A series of randomized controlled trials have demonstrated that screening for colorectal cancer (CRC) using the fecal occult blood (FOB) test can decrease mortality from this disease. These findings were used to develop an actuarial model to estimate the impact that a FOB screening program for colorectal cancer would have on the Canadian population. METHODS: The mortality experience of the year 2000 cohort of Canadians fifty to seventy-four years of age, with follow-up extending to 2010, was modelled according to three scenarios: no screening, annual screening, biennial screening. The primary screening tool was the FOB test using unrehydrated samples, with follow-up of positive test results using colonoscopy. The framework of the model was developed based on published findings from the relevant randomized controlled trials, available data, and a literature review that yielded parameter values for some model items. RESULTS: During the 10-year follow-up of the cohort, we estimated that 4,444 and 2,827 deaths would be averted with annual and biennial FOB screening, respectively. We estimated that for an annual FOB screening program, approximately 3,400 FOB tests would be required to prevent one death, whereas 2,700 tests would be required within a biennial program. CONCLUSIONS: Our analysis documents the population health impact of using the FOB test to screen for CRC. Additional information on the natural history of the disease, and Canadian pilot data are needed to better model the effectiveness of population-based FOB screening programs.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".