Factors Associated with Breast Cancer Mortality after Local Recurrence
Bibliographic record
Abstract
PURPOSE: We aimed to identify risk factors for mortality after local recurrence in women treated for invasive breast cancer with breast-conserving surgery. EXPERIMENTAL DESIGN: Our prospective cohort study included 267 women who were treated with breast-conserving surgery at Women's College Hospital from 1987 to 1997 and who later developed local recurrence. Clinical information and tumour receptor status were abstracted from medical records and pathology reports. Patients were followed from the date of local recurrence until death or last follow-up. Survival analysis used a Cox proportional hazards model. RESULTS: Among the 267 women with a local recurrence, 97 (36.3%) died of breast cancer within 10 years (on average 2.6 years after the local recurrence). The actuarial risk of death was 46.1% at 10 years from recurrence. In a multivariable model, predictors of death included short time from diagnosis to recurrence [hazard ratio (hr) for <5 years compared with ≥10 years: 3.40; 95% confidence interval (ci): 1.04 to 11.1; p = 0.04], progesterone receptor positivity (hr: 0.35; 95% ci: 0.23 to 0.54; p < 0.001), lymph node positivity (hr: 2.1; 95% ci: 1.4 to 3.3; p = 0.001), and age at local recurrence (hr for age >45 compared with age ≤45 years: 0.61; 95% ci: 0.38 to 0.95; p = 0.03). CONCLUSIONS: The risk of death after local recurrence varies widely. Risk factors for death after local recurrence include node positivity, progesterone receptor negativity, young age at recurrence, and short time from diagnosis to recurrence.
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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 | 0.000 | 0.002 |
| 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) | 0.001 | 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".