Supportive and palliative care for metastatic breast cancer: Resource allocations in low- and middle-income countries. A Breast Health Global Initiative 2013 consensus statement
Bibliographic record
Abstract
Many women diagnosed with breast cancer in low- and middle-income countries (LMICs) present with advanced-stage disease. While cure is not a realistic outcome, site-specific interventions, supportive care, and palliative care can achieve meaningful outcomes and improve quality of life. As part of the 5th Breast Health Global Initiative (BHGI) Global Summit, an expert international panel identified thirteen key resource recommendations for supportive and palliative care for metastatic breast cancer. The recommendations are presented in three resource-stratified tables: health system resource allocations, resource allocations for organ-based metastatic breast cancer, and resource allocations for palliative care. These tables illustrate how health systems can provide supportive and palliative care services for patients at a basic level of available resources, and incrementally add services as more resources become available. The health systems table includes health professional education, patient and family education, palliative care models, and diagnostic testing. The metastatic disease management table provides recommendations for supportive care for bone, brain, liver, lung, and skin metastases as well as bowel obstruction. The third table includes the palliative care recommendations: pain management, and psychosocial and spiritual aspects of care. The panel considered pain management a priority at a basic level of resource allocation and emphasized the need for morphine to be easily available in LMICs. Regular pain assessments and the proper use of pharmacologic and non-pharmacologic interventions are recommended. Basic-level resources for psychosocial and spiritual aspects of care include health professional and patient and family education, as well as patient support, including community-based peer support.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".