The effect of Paget disease on axillary lymph node metastases and survival in invasive ductal carcinoma
Bibliographic record
Abstract
BACKGROUND: The objective of this study was to examine the effect of Paget disease (PD) on axillary lymph node metastases and survival in patients who had concomitant invasive ductal carcinoma (PD-IDC). METHODS: The Surveillance, Epidemiology, and End Results (SEER) database was used to identify women who were diagnosed with PD-IDC from 2000 to 2011, comparing baseline demographic and tumor characteristics with those who were diagnosed with IDC alone during the same period. Multivariable logistic regression was used to examine the association of PD-IDC with axillary lymph node metastasis, and breast cancer-specific survival and overall survival were compared between the PD-IDC and IDC groups using the Kaplan-Meier method and Cox proportional hazards regression. RESULTS: The study cohort included 1102 patients with PD-IDC and 302,242 controls with IDC alone. PD-IDC tumors were more likely to be centrally located (26.9% vs 5.5%; P < .001), high grade (63.5% vs 40.3%; P < .001), >2 cm in greatest dimension (47.1% vs 35.7%; P < .001), and estrogen/progesterone receptor-negative (45.2% vs 22.1%; P < .001). In adjusted analyses, patients with PD-IDC had higher odds of axillary lymph node metastasis (odds ratio, 1.83; P < .001). The unadjusted 10-year breast cancer-specific and overall survival rates were lower for the PD-IDC group compared with the IDC-alone group, although, after adjusting for disease stage, tumor characteristics, and local therapy, no significant differences in mortality risk were observed between the 2 groups (hazard ratio, 0.91; P = .24). CONCLUSIONS: PD-IDC is associated with an increased risk of axillary lymph node metastasis, but not with inferior survival, compared with IDC alone after adjustment for other disease factors.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".