Treatment Disparities, Heterogeneities, and Barriers to Access for Patients with Hormone Receptor-Positive, Human Epidermal Growth Factor Receptor 2-Negative Metastatic Breast Cancer: A National Survey from Brazil
Bibliographic record
Abstract
Breast cancer (BC) is the most common malignancy among Brazilian women, with a high percentage of the cases diagnosed at advanced or metastatic stages (mBC). In Brazil, where 75% of the population depends on the resource-limited public health system (SUS), mBC poses significant treatment challenges and disparities. To characterize this scenario, we conducted an online survey assessing treatment strategies available for HER2-negative, hormone receptor (HR)-positive mBC across public and private health systems. The 48-question survey addressed topics such as waiting time (WT) from oncology unit entry to treatment initiation, availability of oncologic medications, and access to palliative and multidisciplinary care teams. Between 2 August 2022 and 30 September 2022, a total of 180 oncologists were invited, and 150 met the inclusion criteria. The median WT for surgery was 60 days in the SUS versus 30 days in the private sector (p < 0.0001), and for chemotherapy, 30 days in the SUS versus 15 days privately (p < 0.0001). Endocrine therapy was the preferred first-line treatment in the SUS (83.3%), while fulvestrant was available to only 48% of respondents. Additionally, specialized palliative care teams were available according to 66% of SUS respondents compared with 82% in the private system (p = 0.001). These findings underscore persistent disparities in mBC treatment, likely driven by limited governmental health investment.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".