Evaluation of Colonoscopy and Sigmoidoscopy Utilization for Colorectal Cancer Screening in Georgia, USA
Bibliographic record
Abstract
Colorectal cancer (CRC) is the third most prevalent cancer, and the second most common cancer-related cause of death in the United States (USA). Timely screening reduces both CRC incidence and mortality. Understanding population behaviors and factors that influence CRC screening is important for directing interventions targeted at reducing CRC rates. The 1997–2018 Behavioral Risk Factor Surveillance System (BRFSS) data were analyzed for trends in colonoscopy and sigmoidoscopy utilization for CRC screening among adults in Georgia, USA. Overall, in Georgia, there has been an increase in the prevalence of colonoscopy and sigmoidoscopy utilization from 48.1% in 1997 to 71.2% in 2018 (AAPC = 2.30, p < 0.001). Compared nationally, this increase was less pronounced (from 41.0% in 1997 to 73.7% in 2018 (AAPC = 2.90, p < 0.001) overall for USA). Logistic regression analysis of the 2018 BRFSS data, adjusting for sociodemographic factors, shows that sex (female vs. male [aOR = 1.20, C.I. = 1.05, 1.38]); marital status (couple vs. single [aOR = 1.20, C.I. = 1.04, 1.39]); healthcare coverage (yes vs. no [aOR = 3.86, C.I. = 3.05, 4.88]); age (60–69 years [aOR = 2.38, C.I. = 2.02, 2.80], 70–79 [aOR = 2.88, C.I. = 2.38, 3.48] vs. 50–59 years); education (high school [aOR = 1.32, C.I. = 1.05, 1.65], some post high school [aOR= 1.63, C.I. = 1.29, 2.06], college graduate [aOR = 2.08, C.I. = 1.64, 2.63] vs. less than high school); and income ($25,000–$49,999 [aOR = 1.24, C.I. = 1.01, 1.51], $50,000+ [aOR = 1.56, C.I. = 1.27, 1.91] vs. <$25,000) were all significantly associated with colonoscopy and sigmoidoscopy utilization. In Georgia, a significant increase over time in colonoscopy and sigmoidoscopy utilization for CRC screening was observed pertaining to the associated sociodemographic factors. The findings from this study may help guide tailored programs for promoting screening among underserved populations.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".