Impact of sequence order of anthracyclines and taxanes in neoadjuvant chemotherapy for breast cancer: Results from a prospective institutional database.
Bibliographic record
Abstract
e12619 Background: There has been growing interest in the optimal sequencing of anthracyclines and taxanes in neoadjuvant chemotherapy (NACT) for breast cancer. However, data comparing efficacy of administering taxanes prior to anthracyclines as opposed to the opposite sequence remains limited and inconsistent. The objective of our study was to assess the impact of sequence order on pathologic and clinical outcomes in a real-world setting. Methods: A prospective institutional database was analyzed to identify all HER2-negative breast cancer patients treated with NACT from 2012 to 2019. Rates of pathologic complete response (pCR), down-staging, and breast-conserving surgery were compared between patients who received anthracyclines followed by taxanes (AC-T) to those who received taxanes followed by anthracyclines (T-AC). Chi-square and independent sample non-parametric tests were used to test for associations between variables and outcomes. Results: Of the 270 patients who met eligibility criteria, 175 (65%) received AC-T and 95 (35%) received T-AC. Median age was 55 (IQR 24-86). Overall, 83% of patients had stage IIB or greater tumors, 40% had grade 3 histology, and 36% had triple-negative disease. Characteristics were balanced between the AC-T and T-AC groups (all p < 0.05). Median duration of treatment with NACT was 102 days (IQR 29-203). Rates of pCR (19% vs 21%, p = 0.750), down-staging (68% vs 61%, p = 0.188), and conversion to breast-conserving surgery (26% vs 20%, p = 0.314) were similar for AC-T vs T-AC, respectively. pCR was higher in triple-negative compared to hormone-positive cases (33% vs 13%, p < 0.001). Conclusions: In this small population-based cohort, sequence order of anthracyclines and taxanes did not demonstrate statistically significant differences in evaluated outcomes from NACT for breast cancer. This supports the current variation in prescribing practice and highlights the need for further studies in this area.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".