Refined estimates of local recurrence risks and the impact of the DCIS score adjusting for clinico-pathological features: Meta-analysis of E5194 and Ontario DCIS cohort studies.
Bibliographic record
Abstract
528 Background: Better tools are needed to estimate the risk of local recurrence (LR; DCIS or invasive) after breast-conserving surgery (BCS) for DCIS to inform treatment decisions. The DCIS Score (DS) was validated as a predictor of LR in E5194 and Ontario DCIS Cohort (ODC) after BCS without radiation (Solin,2013; Rakovitch,2015). We performed a meta-analysis (MA) combining data from E5194 and ODC with additional follow-up from E5194 adjusting for pertinent clinico-pathologic factors to provide refined prediction estimates of LR risk after BCS alone. Methods: The MA used data from E5194 and ODC. Patients with positive margins and multifocality were excluded. Identical Cox regression models were fit including age at diagnosis ( < 50, ≥50 yr), tumor size (1cm, > 1cm), DCIS Score and year of surgery (before vs after 2000). Grade was not significant. MA was used to calculate precision-weighted estimates of 10 year LR risk by DS. Results: Combined cohort includes 773 pts (tamoxifen used in 20% E5194, 17% of ODC > 65 yr). The DS and the clinico-pathologic variables age, tumor size and year provided independent prognostic information on 10 yr LR risk (p≤.009). Hazard ratios from E5194 and ODC cohorts were similar for tumor size ≤1 vs. > 1cm (1.45, 1.47), age ≥50 vs. < 50 yr (0.61, 0.84) and surgery year after 2000 (0.67, 0.49). 10 yr LR risks by combinations of age, tumor size, and DS are detailed in Table. For patients ≥50 yr with tumors ≤1cm and low risk DS, the 10 yr LR risks range from 5.3-10.0%. A high risk DS is associated with a higher 10 yr predicted risk of LR in all subsets. 10 yr risk of contralateral BC was 5.4%. Conclusions: This MA provides refined estimates of 10 yr LR risk after BCS alone for DCIS. Adding clinico-pathologic factors to the DCIS Score provides enhanced prognostic LR risk estimates to guide individualized treatment decision-making. [Table: see text]
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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 | 0.032 | 0.036 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.035 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".