MétaCan
Menu
Back to cohort
Record W2072896120 · doi:10.1186/1471-2407-10-154

Breast cancer treatment and ethnicity in British Columbia, Canada

2010· article· en· W2072896120 on OpenAlexafffundabout
Parvin Yavari, Maria Cristina Barroetavena, T. Greg Hislop, Chris Bajdik

Bibliographic record

VenueBMC Cancer · 2010
Typearticle
Languageen
FieldMedicine
TopicGlobal Cancer Incidence and Screening
Canadian institutionsBC Cancer Agency
FundersBC Cancer AgencyCanadian Institutes of Health ResearchShahid Beheshti University of Medical SciencesMichael Smith Health Research BC
KeywordsBreast cancerMedicineEthnic groupSurgical oncologyCancer registryCancerIncidence (geometry)DemographyPopulationStage (stratigraphy)Psychological interventionGynecologyInternal medicineEnvironmental health

Abstract

fetched live from OpenAlex

BACKGROUND: Racial and ethnic disparities in breast cancer incidence, stage at diagnosis, survival and mortality are well documented; but few studies have reported on disparities in breast cancer treatment. This paper compares the treatment received by breast cancer patients in British Columbia (BC) for three ethnic groups and three time periods. Values for breast cancer treatments received in the BC general population are provided for reference. METHODS: Information on patients, tumour characteristics and treatment was obtained from BC Cancer Registry (BCCR) and BC Cancer Agency (BCCA) records. Treatment among ethnic groups was analyzed by stage at diagnosis and time period at diagnosis. Differences among the three ethnic groups were tested using chi-square tests, Fisher exact tests and a multivariate logistic model. RESULTS: There was no significant difference in overall surgery use for stage I and II disease between the ethnic groups, however there were significant differences when surgery with and without radiation were considered separately. These differences did not change significantly with time. Treatment with chemotherapy and hormone therapy did not differ among the minority groups. CONCLUSION: The description of treatment differences is the first step to guiding interventions that reduce ethnic disparities. Specific studies need to examine reasons for the observed differences and the influence of culture and beliefs.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.061
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0010.000

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 head0.040
GPT teacher head0.315
Teacher spread0.276 · 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.

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

Citations12
Published2010
Admission routes3
Has abstractyes

Explore more

Same venueBMC CancerSame topicGlobal Cancer Incidence and ScreeningFrench-language works237,207