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Record W2335163657 · doi:10.1158/1538-7445.am10-990

Abstract 990: Fundamental causes of colorectal cancer outcomes

2010· article· en· W2335163657 on OpenAlexaff
Andrew C. Wang, Sean Clouston, Marcie S. Rubin, Cynthia G. Colen, Bruce G. Link

Bibliographic record

VenueCancer Research · 2010
Typearticle
Languageen
FieldMedicine
TopicColorectal Cancer Screening and Detection
Canadian institutionsConcordia UniversityMcGill University
Fundersnot available
KeywordsMedicineColorectal cancerInequalityDemographyHealth careCensusCancerGerontologyFamily medicinePopulationInternal medicineEnvironmental healthLawSociology

Abstract

fetched live from OpenAlex

Abstract Introduction Colorectal Cancer is a major cause of mortality with 16.2 people out of 10,000 dying in 2009. Treatments for colorectal cancer exist, with screening done by physicians at clinics and hospitals around the US. Social inequalities in Colorectal Cancer mortality are well studied. However, three theories have arisen that may help to explain why these inequalities have arisen. Fundamental Cause Theory posits that these inequalities arise due to unequal access to resources while this may work in part with differential access to healthcare, and finally differential Diffusion of Knowledge is posited to speed and slow uptake of new medical innovations. Method Using administrative and census data from 2005, mortality rates per county in 3139 counties were stratified by socio-economic status (SES), volume of acute care hospitals (ACHs), volume of primary care physicians (PCPs), and groups of states considered slow to fast diffusion. We controlled for race and gender. Preliminary Results There were 4,683 ACHs and 278,961 PCPs in the analyses. White male averaged 6.42 deaths per 10,000 people, White female at 4.27, Black male at 4.42, Black female at 3.24, Other male at 2.14, and Other female at 2.93. For hospital volume, the average mortality for counties with zero acute hospitals was 7.32 deaths per 10,000, one hospital was 6.20, two hospitals was 6.44, three to fifty hospitals was 5.62, and fifty-five to eighty-nine hospitals was 4.55. For counties with PCPs, areas with zero to four PCPs had an average mortality of 7.48 deaths per 10,000 people, five to thirteen PCPs was 6.06, fourteen to fifty PCPs was 6.17, and fifty-one to eight thousand eight hundred sixty three PCPs was 5.93. The average mortality for PCPs per 100,000 between 0-50 was 7.16 per 10,000, 51-100 was 6.10, 101-149 was 5.78, and more than 150 was 5.08. Counties with high SES and few hospitals had an average mortality of 6.64 per 10,000, where high SES and high volume of hospitals had 5.62, low SES and low volume had 6.19, and low SES and high volume had 7.34. States with slow diffusion had a mortality of 6.33 per 10,000, medium-slow had 6.13, medium-fast had 6.61, and fast had 5.86. Conclusion In this study, we show varying support for each of the three major theories. Fundamental cause theory suggests that SES was correlated with lower mortality rates, but SES played the greatest role in counties with large numbers of hospitals and primary care physicians. Access to healthcare clearly mattered, with more hospitals and primary care physicians correlating to lower colorectal mortality rates. Finally, being in an area typified as quick diffusing was related to lower mortality. This study thus suggests that fundamental cause theory works in part through access to health care. There are long term implications for policy makers looking to reduce social inequalities in colorectal cancer mortality. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 101st Annual Meeting of the American Association for Cancer Research; 2010 Apr 17-21; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2010;70(8 Suppl):Abstract nr 990.

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)0.004
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 score0.016
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0100.001

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 head0.094
GPT teacher head0.449
Teacher spread0.354 · 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
Published2010
Admission routes1
Has abstractyes

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