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Record W4385445221 · doi:10.3390/curroncol30080527

Current Challenges and Disparities in the Delivery of Equitable Breast Cancer Care in Canada

2023· review· en· W4385445221 on OpenAlexaffvenueabout
Emily B. Jackson, Christine Simmons, Stephen Chia

Bibliographic record

VenueCurrent Oncology · 2023
Typereview
Languageen
FieldMedicine
TopicGlobal Cancer Incidence and Screening
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMedicineBreast cancerCurrent (fluid)Healthcare deliveryFamily medicineData scienceCancerHealth careEconomic growthInternal medicineComputer science

Abstract

fetched live from OpenAlex

Recent exciting advances in the diagnosis and management of breast cancer have improved outcomes for Canadians diagnosed and living with breast cancer. However, the reach of this progress has been uneven; disparities in accessing care across Canada are increasingly being recognized and are at risk of broadening. Members of racial minority groups, economically disadvantaged individuals, or those who live in rural or remote communities have consistently been shown to experience greater challenges in accessing 'state of the art' cancer care. The Canadian context also presents unique challenges-vast geography and provincial jurisdiction of the delivery of cancer care and drug funding create significant interprovincial differences in the patient experience. In this commentary, we review the core concepts of health equity, barriers to equitable delivery of breast cancer care, populations at risk, and recommendations for the advancement of health equity in the Canadian cancer system.

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)0.003
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.083
Threshold uncertainty score0.290

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.006
Science and technology studies0.0020.002
Scholarly communication0.0040.001
Open science0.0020.001
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0030.000

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 head0.414
GPT teacher head0.488
Teacher spread0.073 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations7
Published2023
Admission routes3
Has abstractyes

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