Breast cancer treatment: A phased approach to implementation
Bibliographic record
Abstract
Optimal treatment outcomes for breast cancer are dependent on a timely diagnosis followed by an organized, multidisciplinary approach to care. However, in many low- and middle-income countries, effective care management pathways can be difficult to follow because of financial constraints, a lack of resources, an insufficiently trained workforce, and/or poor infrastructure. On the basis of prior work by the Breast Health Global Initiative, this article proposes a phased implementation strategy for developing sustainable approaches to enhancing patient care in limited-resource settings by creating roadmaps that are individualized and adapted to the baseline environment. This strategy proposes that, after a situational analysis, implementation phases begin with bolstering palliative care capacity, especially in settings where a late-stage diagnosis is common. This is followed by strengthening the patient pathway, with consideration given to a dynamic balance between centralization of services into centers of excellence to achieve better quality and decentralization of services to increase patient access. The use of resource checklists ensures that comprehensive therapy or palliative care can be delivered safely and effectively. Episodic or continuous monitoring with established process and quality metrics facilitates ongoing assessment, which should drive continual process improvements. A series of case studies provides a snapshot of country experiences with enhancing patient care, including the implementation of national cancer control plans in Kenya, palliative care in Romania, the introduction of a 1-stop clinic for diagnosis in Brazil, the surgical management of breast cancer in India, and the establishment of a women's cancer center in Ghana.
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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 | 0.148 | 0.067 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.006 | 0.018 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".