Guideline implementation for breast healthcare in low- and middle-income countries
Bibliographic record
Abstract
A key determinant of breast cancer outcome in any population is the degree to which cancers are detected at early stages of disease. Populations in which cancers are detected at earlier stages have lower breast cancer mortality rates. The Breast Health Global Initiative (BHGI) held its third Global Summit in Budapest, Hungary in October 2007, bringing together internationally recognized experts to address the implementation of breast healthcare guidelines for early detection, diagnosis, and treatment in low- and middle-income countries (LMCs). A multidisciplinary panel of experts specifically addressed the implementation of BHGI guidelines for the early detection of disease as they related to resource allocation for public education and awareness, cancer detection methods, and evaluation goals. Public education and awareness are the key first steps, because early detection programs cannot be successful if the public is unaware of the value of early detection. The effectiveness and efficiency of screening modalities, including screening mammography, clinical breast examination (CBE), and breast self-examination, were reviewed in the context of resource availability and population-based need by the panel. Social and cultural barriers should be considered when early detection programs are being established, and the evaluation of early detection programs should include the use of well developed, methodologically sound process metrics to determine the effectiveness of program implementation. The approach and scope of any screening program will determine the success of any early detection program as measured by cancer stage at diagnosis and will drive the breadth of resource allocation needed for program implementation.
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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 | 0.049 | 0.100 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".