Screening Colonoscopy Uptake Among Adult Stroke Survivors: Findings from the 2022 BRFSS Data
Bibliographic record
Abstract
Background/Objectives: Colorectal cancer (CRC) is the second leading cause of cancer-related deaths globally. Screening for cancer helps to prevent comorbid conditions among individuals with chronic medical conditions, such as stroke. The gold standard for CRC screening is colonoscopy. Stroke is the fifth leading cause of death in the United States and a leading cause of long-term disability. This study examined the prevalence of screening colonoscopy among individuals who reported ever having had a stroke (stroke survivors). Methods: The 2022 Behavioral Risk Factor Surveillance System (BRFSS) data were analyzed for the weighted prevalence and odds of screening colonoscopy uptake among adults aged 45 years and older, based on having had a stroke and socioeconomic status. Results: Almost 6% (n = 16,371) of the adults included in the analysis (N = 285,329) reported having had a stroke, and the weighted prevalence of screening colonoscopy for this group was 73.3% compared to 67.8% for those without stroke. Stroke survivors were 1.3 times more likely to have had a screening colonoscopy compared to those without a history of stroke. Higher odds of screening colonoscopy uptake were observed among stroke survivors that were older than 45–49 years old, with high school or greater than high school education. Stroke survivors that were multiracial and without health insurance coverage had lower odds of screening colonoscopy uptake compared to those that were white and had health insurance coverage, respectively. Conclusions: Though adult stroke survivors, compared to those without a stroke, are more likely to report having had a screening colonoscopy, differences in screening colonoscopy uptake were observed among subgroups of this population based on sociodemographic status. Tailored interventions are needed for increasing screening colonoscopy uptake among disadvantaged subgroups.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".