Organized Colorectal Cancer Screening in Integrated Health Care Systems
Bibliographic record
Abstract
Colorectal cancer (CRC) is an ideal target for early detection and prevention through screening. Noninvasive screening options are the guaiac fecal occult blood test and the fecal immunochemical test. Organized screening offers the promise of uniformly delivering screening to all members of a population who are eligible and due. Organized screening is defined as an explicit policy with defined age categories, method, and interval for screening in a defined target population with a defined implementation and quality assurance structure, and tracking of cancer in the population. The UK National Health Service; the Ontario, Canada Ministry of Health and Long-Term Care; and the US Veteran's Health Administration have used varied organized approaches to deliver guaiac fecal occult blood test screening to their populations. Kaiser Permanente Northern California began CRC screening in the 1960s, initially using flexible sigmoidoscopy. Implementation of organized fecal immunochemical test outreach was associated with improved Healthcare Effectiveness Data and Information Set CRC screening rates between 2005 and 2010 from 37% to 69% and from 41% to 78% in the commercial and Medicare populations, respectively. Organized fecal immunochemical test screening has been associated with an increase in annually detected CRCs, almost entirely because of increased detection of localized-stage cancers.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".