Association of local capacity for endoscopy with individual use of colorectal cancer screening and stage at diagnosis
Bibliographic record
Abstract
BACKGROUND: Limited capacity for endoscopy in areas in which African Americans and Hispanics live may be a reason for persistent disparities in colorectal cancer (CRC) screening and stage at diagnosis. METHODS: The authors linked data from the National Health Interview Survey on the use of CRC screening and data from Surveillance, Epidemiology, and End Results-Medicare on CRC stage with measures of county capacity for colonoscopy and sigmoidoscopy (endoscopy) derived from Medicare claims. RESULTS: Hispanics lived in counties with less capacity for endoscopy than African Americans or whites (for National Health Interview Survey, an average of 1224, 1569, and 1628 procedures per 100,000 individuals aged > or = 50 years, respectively). Individual use of CRC screening increased modestly as county capacity increased. For example, as the number of endoscopies per 100,000 residents increased by 750, the odds of being screened increased by 4%. Disparities in screening were mitigated or diminished by adjustment for area endoscopy capacity, racial/ethnic composition, and socioeconomic status. Similarly, among individuals with CRC, those who lived in counties with less endoscopy capacity were marginally less likely to be diagnosed at an early stage. Adjustment for area characteristics diminished disparities in stage for Hispanics compared with whites but not African Americans. CONCLUSIONS: Increasing the use of CRC screening may require interventions to improve capacity for endoscopy in some areas. The characteristics of the area where an individual resides may in part mediate disparities in CRC screening use for both African Americans and Hispanics, and disparities in cancer stage for Hispanics.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".