Estimating Long-Term Crude Probability of Death among Young Breast Cancer Patients: A Bayesian Approach
Why this work is in the frame
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Bibliographic record
Abstract
AIMS AND BACKGROUND: Bayesian survival analysis was applied to assess the long-term survival and probability of death due to breast cancer (BC) in Girona, the Spanish region with the highest BC incidence. METHODS: A Bayesian autoregressive model was implemented to compare survival indicators between the periods 1985-1994 and 1995-2004. We assessed the long-term excess hazard of death, relative survival (RS), and crude probability of death due to BC (PBC) up to 20 years after BC diagnosis, reporting the 95% credible intervals (CI) of these indicators. RESULTS: Patients diagnosed from 1995 onwards showed lower 20-year excess hazards of death than those diagnosed earlier (RS during 1985-1994: local stage: 76.6%; regional stage: 44.9%; RS during 1995-2004: local stage: 85.2%; regional stage: 57.0%). The PBC after 20 years of BC diagnosis for patients diagnosed in 1995 and after might reach 14.4% (95% CI: 8.9%-21.2%) in local stage and 41.0% (95% CI: 36.1%-47.1%) in regional stage. CONCLUSIONS: The method presented could be useful when dealing with population-based survival data from a small region. Better survival prospects were found in patients diagnosed after 1994, although we detected a non-decreasing long-term excess hazard of death, suggesting that these patients have higher mortality than the general population even 10 years after the diagnosis of BC.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 it