Correlation between the DCIS Score and traditional clinicopathologic features in the prospectively-designed Ontario population-based validation study.
Bibliographic record
Abstract
581 Background: In the Ontario population based study, the DCIS Score was significantly associated with 10 year risk of an ipsilateral local recurrence (LR - in situ or invasive carcinoma) in women treated with breast conserving surgery (BCS) without radiation (RT) (P < 0.001). Here we evaluate correlation between DCIS Score and clinicopathologic (CP) features in the same cohort, and whether DCIS Score provides independent recurrence risk information. Methods: The study population included 571 women diagnosed with DCIS in the province of Ontario from 1994 – 2003 prospectively selected for treatment with BCS without RT. CP variables examined included age at diagnosis, DCIS tumor size, DCIS nuclear grade, comedo necrosis (absent, focal, or extensive), histologic type, multifocality, and surgical margin width. The association between DCIS Score and CP variables was examined by spearman rank correlation, and proportional hazards regression models were used to determine variables significantly associated with LR. Results: Tumor size (p = 0.002), multifocality (p < 0.001), histologic type (p = 0.005), and nuclear grade (p = 0.04) were significantly associated with LR. In a multivariable analysis, including significant CP covariates, the DCIS Score was statistically significantly associated with LR (p = 0.02). DCIS Score was moderately correlated with grade (rs= 0.47; 95% CI 0.41,0.54), comedo necrosis (rs= 0.43; CI 0.36,0.50), tumor size (rs= 0.24; CI 0.13,0.35), and multifocality (rs= 0.11; CI 0.03,0.19) but not other features. All CP subgroups showed a wide range of DCIS Scores in each subgroup. Conclusions: DCIS Score is only moderately correlated with grade, comedo necrosis, tumor size, and multifocality. DCIS Score provides recurrence risk information independent of CP features, and quantifies risk of local recurrence in individuals treated by BCS alone, validating previous findings from the E5194 clinical trial
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".