Breast Cancer Care in Developing Countries
Bibliographic record
Abstract
BACKGROUND: Breast cancer is the commonest cancer of women the world over, and its incidence is rising, especially in developing countries, where the disease poses a major health care challenge. This growing incidence in developing countries reflects the advanced stage at diagnosis, low levels of public awareness of the risk for the disease, and poor medical infrastructure and expertise, with the resultant poor treatment outcomes. METHODS: This article provides a collective edited summary of the presentations at the symposium titled "Breast Cancer Care in Developing Countries," held as part of the Breast Surgery International program at the International Surgical week 2007, Montreal, Canada, August 2007. The aim of the presentations was to bring out the diverse clinical pathological and outcomes-related facts of breast cancer care available to women in several countries. As the incidence of breast cancer continues to rise steadily in the developing world, the lack of awareness of this disease and the absence of breast cancer screening programs make it almost certain that the majority of breast cancers are diagnosed at an advanced stage. In addition, the quality of care available for breast cancer patients varies widely according to where the patient is treated. RESULTS: Though there are some centers of excellence providing multimodality protocol-based treatment on a par with the best anywhere in the world, most breast cancer patients receive inadequate and inappropriate treatment because of a lack of high-quality infrastructure-and sometimes skills-and, above all, because of limited financial resources. CONCLUSIONS: In countries where these limitations are present, there is a need to emphasize public health education, promoting early diagnosis. In addition, resources must be directed toward the creation of more public facilities for cancer treatment. As these goals are met, it is likely that there will be a much-needed improvement in breast cancer care in developing countries.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.025 | 0.003 |
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".