Utilization of neoadjuvant chemotherapy in high‐risk, node‐negative early breast cancer
Bibliographic record
Abstract
BACKGROUND: Controversy exists regarding the optimal sequence of chemotherapy among women with operable node-negative breast cancers with high-risk tumor biology. We evaluated national patterns of neoadjuvant chemotherapy (NACT) use among women with early-stage HER2+, triple-negative (TNBC), and high-risk hormone receptor-positive (HR+) invasive breast cancers. METHODS: Women ≥18 years with cT1-2/cN0 HER2+, TNBC, or high recurrence risk score (≥31) HR+ invasive breast cancers who received chemotherapy were identified in the National Cancer Database (2010-2016). Cochran-Armitage and logistic regression examined temporal trends and likelihood of undergoing NACT versus adjuvant chemotherapy based on patient age and molecular subtype. RESULTS: Overall, 96,622 patients met study criteria; 25% received NACT and 75% underwent surgery first, with comparable 5-year estimates of overall survival (0.90, 95% CI 0.892-0.905 vs 0.91, 95% CI 0.907-0.913). During the study period, utilization of NACT increased from 14% to 36% and varied according to molecular subtype (year*molecular subtype p < 0.001, p-corrected < 0.001). Women with HER2+ (OR 4.17, 95% CI 3.70-4.60, p < 0.001, p-corrected < 0.001) and TNBC (OR 3.81, 95% CI 3.38-4.31, p < 0.001, p-corrected < 0.001) were more likely to receive NACT over time, without a change in use among those with HR+ disease (OR 1.58, 95% CI 0.88-2.87, p = 0.13, p-corrected = 0.17). CONCLUSION: Among women with early-stage triple-negative and HER2+ breast cancers, utilization of NACT increased over time, a trend that correlates with previously reported improved rates of pCR and options post-neoadjuvant treatment with residual disease. Future research is needed to better understand multidisciplinary decisions for NACT and implications for breast cancer patients.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".