Bibliographic record
Abstract
As recently as two decades ago breast cancer was not a significant public health concern in Latin America and the Caribbean (LAC). However, mortality rates from breast cancer have been increasing for at least 40 years in most LAC countries. Socioeconomic development and consequent changes in reproductive behaviors over the past 50 years are thought to have contributed to the increased risk of breast cancer. Socioeconomic development has also increased women's health awareness and therefore the demand for quality services. In industrialized countries, screening and widely available, high-quality treatment protocols are being implemented as the main strategy for breast cancer control. Studies show that out of three available screening methods (mammography, clinical breast examination, and breast self-examination), only mammography for women 50-69 years of age has been effective at reducing mortality, and has done so by an estimated 23%. While there is much controversy about the benefits and cost-effectiveness of mammography screening for women aged 40-49, some countries, including Australia, the United States of America, and four European nations, recommend that physicians assess the need for it on an individual basis. A survey that we conducted of LAC countries shows that most of their breast cancer screening policies are not justified by available scientific evidence. Moreover, as seen by relatively high mortality/incidence ratios, breast cancer cases are not being adequately managed in many LAC countries. Before further developing screening programs, these countries need to evaluate the feasibility of designing and implementing appropriate treatment guidelines and providing wide access to diagnostic and treatment services. Given the relevance of breast cancer in Latin America and the Caribbean today, it is crucial that both women and health care providers have access to up-to-date information on which to base their decisions.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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 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".