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Record W2165103978 · doi:10.2105/ajph.2009.173112

Effects of Socioeconomic Status on Colon Cancer Treatment Accessibility and Survival in Toronto, Ontario, and San Francisco, California, 1996–2006

2010· article· en· W2165103978 on OpenAlexafffundabout
Kevin M. Gorey, Isaac Luginaah, Emma Bartfay, Karen Y. Fung, Eric J. Holowaty, Frances C. Wright, Caroline Hamm, Sindu Kanjeekal

Bibliographic record

VenueAmerican Journal of Public Health · 2010
Typearticle
Languageen
FieldMedicine
TopicGlobal Cancer Incidence and Screening
Canadian institutionsUniversity of Windsor
FundersCanadian Institutes of Health Research
KeywordsSocioeconomic statusMedicineGerontologyCancer registryDemographyPopulationColorectal cancerCancerEnvironmental healthInternal medicine

Abstract

fetched live from OpenAlex

OBJECTIVES: We examined the differential effects of socioeconomic status on colon cancer care and survival in Toronto, Ontario, Canada, and San Francisco, California. METHODS: We analyzed registry data for colon cancer patients from Ontario (n = 930) and California (n = 1014), diagnosed between 1996 and 2000 and followed until 2006, on stage, surgery, adjuvant chemotherapy, and survival. We obtained socioeconomic data for individuals' residences from population censuses. RESULTS: Income was directly associated with lymph node evaluation, chemotherapy, and survival in San Francisco but not in Toronto. High-income persons had better survival rates in San Francisco than in Toronto. After adjustment for stage, survival was better for low-income residents of Toronto than for those of San Francisco. Middle- to low-income patients were more likely to receive indicated chemotherapy in Toronto than in San Francisco. CONCLUSIONS: Socioeconomic factors appear to mediate colon cancer care in urban areas of the United States but not in Canada. Improvements are needed in screening, diagnostic investigations, and treatment access among low-income Americans.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.298
Threshold uncertainty score0.610

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.035
GPT teacher head0.371
Teacher spread0.336 · 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 teacher head, 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

Citations55
Published2010
Admission routes3
Has abstractyes

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