Guideline implementation for breast healthcare in low- and middle-income countries
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it