The impact of stratifying by family history in colorectal cancer screening programs
Bibliographic record
Abstract
In the province-wide colorectal cancer (CRC) screening program in Ontario, Canada, individuals with a family history of CRC are offered colonoscopy screening and those without are offered guaiac fecal occult blood testing (gFOBT, Hemoccult II). We used microsimulation modeling to estimate the cumulative number of CRC deaths prevented and colonoscopies performed between 2008 and 2038 with this family history-based screening program, compared to a regular gFOBT program. In both programs, we assumed screening uptake increased from 30% (participation level in 2008 before the program was launched) to 60%. We assumed that 11% of the population had a family history, defined as having at least one first-degree relative diagnosed with CRC. The programs offered screening between age 50 and 74 years, every two years for gFOBT, and every ten years for colonoscopy. Compared to opportunistic screening (2008 participation level kept constant at 30%), the gFOBT program cumulatively prevented 6,700 more CRC deaths and required 570,000 additional colonoscopies by 2038. The family history-based screening program increased these numbers to 9,300 and 1,100,000, a 40% and 93% increase, respectively. If biennial gFOBT was replaced with biennial fecal immunochemical test (FIT), annual Hemoccult Sensa or five-yearly sigmoidoscopy screening, both the added benefits and colonoscopies required would decrease. A biennial gFOBT screening program that identifies individuals with a family history of CRC and recommends them to undergo colonoscopy screening would prevent 40% (range in sensitivity analyses: 20-51%) additional deaths while requiring 93% (range: 43-116%) additional colonoscopies, compared to a regular gFOBT screening program.
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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 | 0.009 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| 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".