Twenty years of breast cancer epidemiology and treatment patterns in São Paulo, Brazil—observed versus expected treatment utilization in a retrospective cohort
Bibliographic record
Abstract
Background: Over half of new breast cancer cases occur in low- and middle-income countries, with disparities in survival outcomes due to late-stage diagnoses, healthcare access gaps, and biological differences. This retrospective cohort study examined trends in survival, stage distribution, and treatment utilization for breast cancer in Brazil, an upper middle-income country. Methods: Patients newly diagnosed with invasive breast cancer between 2000 and 2019 were identified from São Paulo's Oncocenter Foundation registry. Data on demographics, diagnosis stage, diagnosis-to-treatment intervals, and treatments received were analyzed in 5-year blocks. Median overall survival was estimated using the Kaplan-Meier method. Actual treatment utilization was compared to model-based estimates of optimal utilization derived from the National Comprehensive Cancer Network Guidelines' Enhanced and Maximal Resource Modules. Findings: We included 125,005 patients, with a median age at diagnosis of 55 years (interquartile range 46-75); 99.4% (n = 124,218) were female. The proportion with early disease remained stable over time (61.7% in 2000-2004, 62.4% 2015-2019). Median overall survival increased from 10.7 years (2000-2004) to 11.7 years (2010-2014); median survival for 2015-2019 was not reached. Median overall survival was 20.8, 15.1, 6.8, and 2.0 years for stages I-IV, respectively. Median diagnosis-to-treatment interval more than doubled over time. From 2000 to 2004 to 2015-2019, chemotherapy use decreased from 71.5% to 68.9%; radiotherapy use decreased from 64.0% to 56.5%, and surgery utilization decreased from 80.3% to 74.8%; endocrine therapy use varied between 54% and 62%. Gaps between observed and model-based estimates of treatment utilization were seen across all stages. Interpretation: Overall survival in patients with breast cancer in São Paulo has improved over time. However, significant treatment gaps and increasing diagnosis-to-treatment intervals suggest systemic barriers to optimal care delivery. Funding: No funding received.
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How this classification was reachedexpand
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) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".