Disparities in Breast Cancer: Private Patients Have Better Outcomes Than Public Patients
Bibliographic record
Abstract
INTRODUCTION: Different outcomes in breast cancer have been reported for low and high socio-economic groups. We present data quantifying disparities between South African public and private patients. METHODS: Records of 240 consecutive patients treated in 2008 in a public versus 97 patients in a private health facility were reviewed for demographic and oncologic data. RESULTS: The average of patients was 56.2 versus 51.9 years (p = 0.032). Stage at presentation was 0 in 0.83 versus 25.8%, I in 4.5 versus 15.5%, II in 41.3 versus 37.1%, III in 37.1 versus 18.6% and IV in 16.3 versus 3.1% public versus private patients. Seventy-three percent of patients were symptomatic versus 57.7%. Of patients with stage 0-III disease, 17.9 versus 20% had simple tumour excision and 7.5 versus 14%, oncoplastic tumour excision. The mastectomy rate was similar (52 vs. 60%), but immediate reconstruction was performed in 10 versus 63%. Public patients were less likely to have radiotherapy. The pathology was similar, 27.2 versus 20, 54 versus 52, 87 versus 61% of patients with stage I, II and III disease, respectively, had chemotherapy. Hormonal therapy for premenopausal patients in private was a LHRH agonist in 9.3%, ovarian ablation/BSO in 11.7% of public patients; biologicals were given in 7.2 versus 0% of patients. Overall survival for public versus private was 66 versus 80% (p < 0.001) months. Better per stage survival of private patients 100 versus 100, 72.7 versus 93.3, 84.8 versus 88.9, 57.3 versus 77.8 and 33 versus 33% for stages 0, I, II, III and IV, did not reach statistical significance. CONCLUSION: The greatest impact on outcome was stage at presentation, but more aggressive therapy for each stage resulted in a trend to better outcome for private patients.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.006 | 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".