Discrepancies in breast cancer’s oncological outcomes between public and private institutions in the southeast region of Brazil: a retrospective cohort study
Bibliographic record
Abstract
Background Brazil is a middle-income country with inequalities in its healthcare system. The disparities between public and private services affect the diagnosis and treatment of patients with breast cancer. The aim of this study is to assess whether disease-free survival (DFS) and overall survival (OS) are different in public and private specialized centers. Patient and methods A retrospective cohort study with 1,545 breast cancer patients diagnosed from 2003 to 2011 at Barretos Cancer Hospital—BCH (public group, N = 1,408) and InORP Oncoclinicas (private group, N = 137) was conducted. A 1:1 propensity score matching (PSM) analysis was used to adjust the differences between the groups’ characteristics (n = 137 in each group). Results The median age at diagnosis was 54.4 years. Estimated DFS rates at 1, 5, and 10 years were 96.0%, 71.8%, and 59.6%, respectively, at BCH and 97.8%, 86.9%, and 78%, respectively, at InORP (HR: 2.09; 95% confidence interval [CI], 1.41–3.10; p < 0.0001). Estimated OS rates at 1, 5, and 10 years were 98.1%, 78.5%, and 65.4%, respectively, at BCH and 99.3%, 94.5%, and 91.9%, respectively, at InORP (HR: 3.84; 95% CI, 2.16–6.82; p < 0.0001). After adjustment by PSM, DFS and OS results in 1, 3, and 5 years remained worse in the public service compared to the private service. Conclusion Patients treated in a public center have worse DFS and OS after a follow-up period of more than 5 years. These results were corroborated after carrying out the PSM.
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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 | 0.002 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".