The impact of Asian race on colorectal cancer screening (CRCS).
Bibliographic record
Abstract
381 Background: While research shows that African Americans and Hispanics frequently receive less CRCS than Whites, few studies have focused on CRCS among Asians. The aims of the current analysis were to 1) compare CRCS between Asians and Whites in a large U.S. population, 2) evaluate for other clinical predictors of CRCS, and 3) examine the impact of health insurance coverage, place of birth and English proficiency on potential racial disparities. Methods: From the 2007 California Health Interview Survey, we identified all Asian (N=2,108) and White (N=23,237) average-risk respondents aged ≥50 years who were eligible for CRCS. Logistic regression was performed to evaluate for differences in CRCS between Asians and Whites. We used stratified and interaction analyses to examine whether associations between race and CRCS were modified by insurance status (insured vs uninsured), birthplace (U.S. vs non-U.S.) or language skills (good vs poor English), while controlling for other confounders. Results: Baseline characteristics were similar between Asians and Whites: mean age was 64 years in both groups; 45% and 47% were male; and 47% and 50% were employed, respectively. Only 58% of Asians and 66% of Whites reported undergoing up-to-date CRCS (p<0.001). In multivariate analyses, female gender and those living in rural areas were less likely to receive CRCS (OR 0.84, 95%CI 0.76-0.93, p=0.001 and OR 0.88, 95%CI 0.81-0.98, p=0.015, respectively).When compared to Whites, Asians also had decreased odds of CRCS (OR 0.82, 95%CI 0.71-0.95, p=0.008), even after adjusting for confounders such as education and income. Stratified analyses revealed that this disparity existed mainly in the insured (OR 0.83, 95%CI 0.72-0.96, p=0.014), but not in the uninsured (OR 0.94, 95%CI 0.43-2.06, p=0.873). The relationship between race and CRCS was not modified by place of birth or English proficiency. Conclusions: Despite its ability to reduce mortality, CRCS is suboptimal in our U.S. population-based cohort of Asians when compared to Whites. The racial disparity was more evident within the insured subset, suggesting that factors unrelated to healthcare access, such as patient preference, physician discretion or patient-physician rapport, may be more important drivers of CRCS among Asians. No significant financial relationships to disclose.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.006 | 0.001 |
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".