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The impact of Asian race on colorectal cancer screening (CRCS).

2011· article· en· W2589701123 on OpenAlexaff
Babak Homayoon, Neal Shahidi, Winson Y. Cheung

Bibliographic record

VenueJournal of Clinical Oncology · 2011
Typearticle
Languageen
FieldMedicine
TopicColorectal Cancer Screening and Detection
Canadian institutionsBC Cancer Agency
Fundersnot available
KeywordsMedicineDemographyLogistic regressionConfoundingMultivariate analysisPopulationEthnic groupHealth equityGerontologyPublic healthEnvironmental healthInternal medicine

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.001

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.

Opus teacher head0.179
GPT teacher head0.492
Teacher spread0.313 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2011
Admission routes1
Has abstractyes

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