Colorectal cancer screening with Fecal Immunochemical Test
Bibliographic record
Abstract
According to the World Health Organization report, colorectal cancer (CRC) is the third most common cancer worldwide. Several CRC screening methods are available that include stool-based tests to detect blood (guaiac fecal occult blood test and fecal immunochemical test-FIT), endoscopic methods (sigmoidoscopy and colonoscopy), imaging methods (computed tomographic (CT) colonography, video capsule endoscopy), and biomarkers. Since 2019, the recommendation Worldwide for colorectal cancer screening in individuals aged 50 to 74 is a fecal immunochemical test (FIT). Erie Shores Health Care (ESHC) serves rural and remote Canadians spanning the Greater Windsor-Essex County area, located in Ontario, which includes Caldwell First Nation, Migrant Agricultural Workers, the Mennonite community, and the un-documented or documented refugee population. We noticed that many individuals attending the ESHC-Surgery facility for colonoscopy were either unaware of the FIT test or were not offered an FIT test before colonoscopy. The objective is to provide insight into the reasons for the under-utilization of the FIT test for CRC screening and guidance for an effective screening strategy for our region. An exploration into the utilization of FIT in the Windsor-Essex region with a retrospective data-driven analysis using the region's existing data is the proposed methodology. This study may aid hospital administration and clinicians in visualizing a realistic snapshot of FIT test utilization in the region, prompting the identification of possible barriers to FIT test utilization that are critical for improving uptake of and adherence to CRC screening. The publication will contribute knowledge on FIT test utilization in rural/remote Canadian healthcare systems.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".