The Impact of Neoadjuvant versus Adjuvant Chemotherapy on Survival Outcomes in Locally Advanced Breast Cancer
Bibliographic record
Abstract
The utility of neoadjuvant chemotherapy is expanding in the treatment of breast cancer. Although individual trials have shown comparable survival between patients receiving neoadjuvant and adjuvant chemotherapy, large-scale data analyses for outcomes in patients with locally advanced breast cancer (LABC) are lacking. We conducted an individual-level statistical analysis using patients from six randomized controlled trials (RCTs) investigating survival outcomes with neoadjuvant versus adjuvant chemotherapy in breast cancer by abstracting and analyzing only the patients with LABC. Individual patient data for 779 patients with LABC were collected from six RCTs. Overall and disease-free survival rates were compared between patients receiving neoadjuvant vs. adjuvant chemotherapy with the Cox hazard model and log-rank statistics. Since chemotoxicity causing delays to surgical care is a potential drawback of neoadjuvant chemotherapy, local cohort data were then employed to assess the actual incidence of this, along with the causes behind any delays to surgery in patients receiving neoadjuvant chemotherapy. A time interval from neoadjuvant chemotherapy to surgery of >8 weeks was investigated in a local cohort of 563 patients, representing all locally treated patients receiving neoadjuvant chemotherapy between 2006 and 2019. The statistical analysis demonstrated no overall or disease-free survival differences in LABC patients receiving neoadjuvant vs. adjuvant chemotherapy (p = 0.96 and 0.74, respectively). Within our cohort, 31 (5.5%) patients treated with neoadjuvant chemotherapy experienced a delay of >8 weeks to surgery, with only 13 (2.3%) attributed to chemotherapy-related complications. Our study provides further support for the paradigm shift towards delivering chemotherapy for breast cancer patients in the neoadjuvant setting.
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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 | 0.009 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".