Breast Cancer Priorities in Limited-Resource Environments: The Price-Efficacy Dilemma in Cancer Care
Bibliographic record
Abstract
Breast cancer has become one of the leading causes of morbidity and mortality in low- and middle-income countries, where 62% of the world's total new cases are diagnosed. Therefore, the productivity loss because of premature death resulting from female breast cancer is also on the rise. The major challenge in low- and middle-income countries is to reduce the proportion of women presenting with advanced-stage disease, a challenge unlikely to be overcome by adoption of expensive national mammography screening programs. Awareness and education campaigns should focus not only on patients and societies but also on policy makers to address and optimize breast cancer care. Adaptation of existing guidelines and prioritization according to local resources are essential to address the unique needs and overcome the unique barriers of each society to facilitate practical implementation and improve outcomes. Emphasis on the principle of a cancer groundshot in addressing value in cancer care is vital to improving access to therapies that are proven to work rather than chasing after new drugs or innovations of doubtful or marginal clinical benefit. Until we have drug-pricing interventions that take into account the local income of each society, we must acknowledge the fact that the delivery of cancer care will never be the same all around the world.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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 teacher head, 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".