Characterization of breast cancer detection programs in the Americas region with a focus on Ecuador
Bibliographic record
Abstract
Introduction: breast cancer is the leading cause of mortality in women in the Americas Region, with 491 000 annual cases and approximately 106 391 deaths. In Ecuador, in 2020, there were 38,2 cases per 100 000 inhabitants, with a mortality rate of 10,9 per 100 000 individuals, emphasizing the importance of early detection and access to effective treatments to reduce the morbimortality associated with this pathology.Methodology: astematic review of 58 922 scientific articles was conducted, from which, applying inclusion and exclusion criteria, 36 publications were selected from databases such as PubMed, BVS, SCOPE, Web of Science, Google Scholar, and health websites of the six representative countries under study: the United States, Canada, Mexico, Uruguay, Brazil, and Ecuador. The collected data measured the impact and characteristics of breast cancer detection programs in relation to the reduction of mortality.Results: fifty percent of selected countries have active breast cancer detection programs, 33 % (equivalent to 2 nations) had protocols and clinical guidelines for prevention, and only one South American country was in the initial stage of implementing a sustainable pilot plan.Conclusion: in the Americas Region, it is crucial for governments to implement organized and accessible programs, ensuring universal access to the diagnosis and treatment of breast cancer. Ecuador must join this initiative, promoting prevention and public health through pragmatic policies to reduce mortality from breast cancer
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".