Sociodemographic and clinical determinants of survival in metastatic breast cancer: A population-based analysis from a Latin American developing country.
Bibliographic record
Abstract
e13125 Background: To evaluate the impact of tumor, treatment, and sociodemographic factors on overall survival (OS) and cancer-specific survival (CSS) in metastatic breast cancer (mBC) using a population-based database from a Latin American developing country. Methods: Patients diagnosed with de novo MBC from 2000 to 2023 were identified in the Fundação Oncocentro São Paulo (FOSP-Brazil) database. OS and CSS were estimated using the Kaplan-Meier method and stratified by treatment, tumor, patient, and sociodemographic characteristics. Cox proportional hazards models were used to identify factors associated with OS and CSS. A Sociodemographic Index (SD-I) was developed to assess its impact on survival outcomes. Results: A total of 14,588 patients were included. The 1-, 3-, and 5-year OS rates were 64%, 32%, and 18%, respectively, while CSS rates were 67%, 36%, and 22%. In multivariate analysis (MVA), significant predictors of OS and CSS included the number of metastatic sites (p < 0.001), radiotherapy to metastases (p < 0.01), educational level (p < 0.01), treatment delay (p < 0.001), public healthcare access (p < 0.01), and treatment center complexity (p = 0.001). Patients were stratified into high, mid, low, and very low SD-I categories based on significant sociodemographic factors. The 5-year OS rates for high, mid, low, and very low SD-I groups were 23.5% (95% CI: 17-33%), 18.6% (95% CI: 17.2-22.1%), 17.3% (95% CI: 16.5-18.2%), and 14.7% (95% CI: 10-21.7%), respectively (p < 0.001). Similar trends were observed for CSS (p < 0.001). Conclusions: This study identifies critical factors influencing survival in MBC and underscores the impact of sociodemographic disparities (SD-I), highlighting the need for targeted interventions to address healthcare inequalities.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".