The Breast Health Global Initiative 2018 Global Summit on Improving Breast Healthcare Through Resource‐Stratified Phased Implementation: Methods and overview
Bibliographic record
Abstract
BACKGROUND: The Breast Health Global Initiative (BHGI) established a series of resource-stratified, evidence-based guidelines to address breast cancer control in the context of available resources. Here, the authors describe methodologies and health system prerequisites to support the translation and implementation of these guidelines into practice. METHODS: In October 2018, the BHGI convened the Sixth Global Summit on Improving Breast Healthcare Through Resource-Stratified Phased Implementation. The purpose of the summit was to define a stepwise methodology (phased implementation) for guiding the translation of resource-appropriate breast cancer control guidelines into real-world practice. Three expert consensus panels developed stepwise, resource-appropriate recommendations for implementing these guidelines in low-income and middle-income countries as well as underserved communities in high-income countries. Each panel focused on 1 of 3 specific aspects of breast cancer care: 1) early detection, 2) treatment, and 3) health system strengthening. RESULTS: Key findings from the summit and subsequent article preparation included the identification of phased-implementation prerequisites that were explored during consensus debates. These core issues and concepts are key components for implementing breast health care that consider real-world resource constraints. Communication and engagement across all levels of care is vital to any effectively operating health care system, including effective communication with ministries of health and of finance, to demonstrate needs, outcomes, and cost benefits. CONCLUSIONS: Underserved communities at all economic levels require effective strategies to deploy scarce resources to ensure access to timely, effective, and affordable health care. Systematically strategic approaches translating guidelines into practice are needed to build health system capacity to meet the current and anticipated global breast cancer burden.
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 | 0.276 | 0.138 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.007 | 0.016 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".