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Record W2565720239

A study design to explore the determinants of breast cancer survival in Ontario’s First Nations women

2007· article· en· W2565720239 on OpenAlexaffabout
A Ritchie, Anna M. Chiarelli, Loraine D. Marrett, Maureen Trudeau, Diane Nishri

Bibliographic record

VenueCancer Epidemiology and Prevention Biomarkers · 2007
Typearticle
Languageen
FieldMedicine
TopicCancer Risks and Factors
Canadian institutionsHealth Sciences CentreUniversity of TorontoCancer Care OntarioSunnybrook Health Science Centre
Fundersnot available
KeywordsBreast cancerMedicineStage (stratigraphy)CancerPopulationDemographyGynecologyOncologyInternal medicineEnvironmental health
DOInot available

Abstract

fetched live from OpenAlex

B51 Background: Recent data have shown that survival after a breast cancer diagnosis is poorer among First Nations women compared to other Ontario women. There are many possible determinants of this survival discrepancy, many of which have not been studied among a Native population. The main purpose of this study is to identify determinants for the poorest breast cancer survival by comparing stage, treatment and other risk factors among First Nations and non-First Nations women diagnosed with breast cancer from 1995-2004 in Ontario. Once these have been determined, actions can be taken to improve the prognosis of First Nations women with breast cancer.
 Objectives: 1)To compare the distribution of stage at diagnosis (stage II+ vs. stage I) for Ontario First Nations and non-First Nations women diagnosed with breast cancer between 1995 to 2004. 2) To compare other potential determinants of survival between the two populations, such as treatment, prognostic features of breast cancer, co-morbidity, and distance from an Integrated Cancer Program by stage at diagnosis. 3) Depending on the number of deaths in the First Nations women at the end of the study, we propose to compare stage specific survival between the two populations.
 Hypotheses: 1) First Nations women are diagnosed with breast cancer at a later stage of disease (either stage II, III or IV) than non-First Nations women in Ontario. 2) First Nations women diagnosed with breast cancer at a later stage in Ontario may differ by the treatment they receive, prognostic features of breast cancer, co-morbidity, and distance from an Integrated Cancer Program compared to the general population. 3) Stage-specific survival among women diagnosed with breast cancer is worse for First Nations women in Ontario compared to general population.
 Study Design: This study employs a case-case design using the cohort of First Nations people in Ontario to identify an estimated 315 women diagnosed with invasive breast cancer between 1995 and 2004. Concurrently, a random sample of 630 non-First Nations women will be selected through the population-based cancer registry at Cancer Care Ontario and matched 2 to 1 on five-year date of diagnosis, age at diagnosis (15-54 vs. 55+), and Integrated Cancer Program first attended. Data on stage at diagnosis, treatment received, risk factors and co-morbid conditions will be collected from medical charts at the provincial Integrated Cancer Programs.
 Analysis: To address the primary objective of the study, stage at diagnosis will be aggregated into a binary variable to obtain the distribution across the two populations. Logistic regression models will be performed to investigate the second study objective. The analyses will test the influence of the independent variables of interest by stage at diagnoses comparing the two populations. We propose a Cox-proportional hazards regression model to assess stage specific survival (for stages 2+) for the third study objective.
 Contribution of Study: This is a unique opportunity to study factors related to breast cancer survival in this Ontario’s First Nations women. This work is expected to inform health care decision makers about where barriers may exist with respect to cancer screening, treatment and surveillance for First Nations women. The results of this study may support improvements of cancer care for this population.

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)0.003
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 score0.656
Threshold uncertainty score0.942

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.194
GPT teacher head0.424
Teacher spread0.230 · 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

Citations0
Published2007
Admission routes2
Has abstractyes

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