Bridging Gaps in Breast Cancer Care: A Breast Cancer Care Quality Index to Improve Outcomes Worldwide
Bibliographic record
Abstract
Background: Breast cancer (BC) care faces challenges in early detection, timely diagnosis and comprehensive management. Disparities persist, with underserved populations facing the greatest barriers. Addressing these requires policies that support consistent, evidence-based practices and enhance healthcare capacity and technology advancements. This document presents the development of the Breast Cancer Care Quality Index (BCCQI), supported by evidence to promote equitable care and improve BC outcomes globally, and discusses its adoption as a strategic tool within National Cancer Control Plans. Methods: A two-part methodology identified challenges in BC care and defined dimensions, targets and indicators for the BCCQI, aligned with the World Health Organization Global Breast Cancer Initiative. A literature review and analysis of existing United Nations (UN) frameworks informed the initial structure of the index, which was later refined through expert feedback from a multidisciplinary panel representing diverse backgrounds and geographies. Findings: The BCCQI is organised into four dimensions, comprising 10 targets and 23 indicators to guide the development of country-specific roadmaps. It should promote progress across key domains: health equity, patient centricity, universal access, care quality and treatment effectiveness. The Index is conceived as a dynamic tool, continuously refined through real-world application and emerging evidence. Interpretation: Despite the previous initiatives, progress has been slow, likely due to practical details and country-specific guidance remaining limited due to scarce real-world evidence. Promoting national ownership and empowering action aligned with local challenges and opportunities, a flexible, strategic framework may help address these gaps.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.028 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".