Temporal trends in colorectal cancer screening (CRCS).
Bibliographic record
Abstract
356 Background: The rate of CRCS continues to be suboptimal. Our study objectives were to characterize temporal trends in CRCS in general and within specific subpopulations and identify clinical and system factors that pose barriers to CRCS. Methods: Data from respondents aged ≥50 years who were classified as average risk (no personal or family history of colorectal cancer) from the 2001 to 2009 California Health Interview Survey were analyzed. Up-to-date CRCS was defined as having had a sigmoidoscopy or colonoscopy within the past 10 years. Using multivariate regression analyses that adjusted for confounders, rates of CRCS over consecutive periods of 2 calendar years were determined. Stratified analyses that explored for effect modification based on gender, ethnicity, smoking history, educational attainment, income level, health insurance status, and urban vs. rural residence were conducted. Results: A total of 126,873 screening-eligible respondents was included: median age was 63 years (range 50-85); 50,303 (46%) were men, and 95,534 (63%) were white. In the entire cohort, only 73,589 (55%) reported up-to-date CRCS. Over time, there was a significant trend towards increased CRCS, ranging from 48% in 2001 to 61% in 2009 (p<0.01). After adjusting for confounding variables, this trend persisted with higher odds of up-to-date CRCS in more recent years (see Table). Specific characteristics were associated with decreased likelihood of CRCS: women (OR 0.63), low education (OR 0.72), poor income (OR 0.84), no insurance (OR 0.44), and rural residence (OR 0.93) (p<0.01 for all). Temporal increases in CRCS were most prominent in particular groups, such as women (OR 3.55 in 2009 vs. 2001) and the uninsured (OR 11.48 in 2009 vs. 2001) (p<0.01 for both). Conclusions: Although CRCS improved over time, there is still room for significant improvement. Temporal increases in CRCS were most substantial among women and the uninsured. Interventions used to improve CRCS in these groups should be harnessed and implemented within other minorities to enhance screening. [Table: see text]
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| 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) | 0.003 | 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".