Clinicopathologic factors of the recurrent tumor predict outcome in patients with ipsilateral breast tumor recurrence
Bibliographic record
Abstract
BACKGROUND: The role of clinicopathologic characteristics of the recurrent tumor in determining survival in a cohort of patients with ipsilateral breast tumor recurrence (IBTR) was investigated. METHODS: Among 6020 women with pT1-T2, pN0-1, M0 treated with breast-conserving surgery from 1989 to 1999, 269 developed isolated IBTR. Ten-year Kaplan-Meier breast cancer-specific survival (BCSS) and overall survival (OS), calculated from date of IBTR, were analyzed according to clinicopathologic characteristics. RESULTS: Factors that were associated with diminished OS and BCSS on univariate analysis were: time to IBTR ≤48 months, lymphovascular invasion positive status, estrogen receptor (ER) negative status, high grade, clinical IBTR detection, biopsy alone, and close/positive margins (all P < .05). On multivariate analysis, time to IBTR ≤48 months (hazard ratio [HR], 1.87, P = .012), lymphovascular invasion positive status (HR, 2.46; P < .001), ER negative status (HR, 2.08; P = .013), high-grade recurrent disease (HR, 1.88; P = .013), and close/positive margins after surgery for IBTR (HR, 1.94; P = .013) retained significance for prediction of diminished OS. When stratified according to number of adverse prognostic features, 10-year OS was 70.4% in patients with 1 factor, 35.8% with 2 factors, and 19.9% with 3 or more factors (P < .001). CONCLUSIONS: Time to recurrence ≤48 months, lymphovascular invasion positive status, ER negative status, high-grade histology, and close/positive margins in association with the recurrent tumor are independent prognostic factors for survival after IBTR. The presence of 2 or more of these features at recurrence is significantly associated with poor OS. These criteria can be used to prognosticate and guide clinical decisions after 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".