Efficacy Of Pegylated Liposomal Doxorubicin-based Neoadjuvant Chemotherapy In Breast Cancer: A Single Center Experience
Bibliographic record
Abstract
PURPOSE: Neoadjuvant chemotherapy using a doxorubicin-based regimen has recently become a common therapeutic option for operable breast cancer. This study aimed to investigate the efficacy of polyethylene glycol-coated liposomal doxorubicin (PLD)-based chemotherapy for breast cancer in neoadjuvant settings. METHODS: A total of 227 female operable breast cancer patients who were diagnosed between January 2009 and December 2017 and completed neoadjuvant PLD-based chemotherapy were retrospectively included. The logistic regression analysis was used to determine the associations between pathologic complete response (pCR) and preoperative clinicopathological characteristics. The breast cancer recurrence rate was estimated using the survival analysis. RESULTS: A higher pCR rate was found in the patients with clinically negative lymph nodes and HER2-enriched patients. Moreover, the patients who achieved pCR also had a better prognosis outcome. A recurrence rate of 11.5% (n=26) was observed during a median follow-up of 11.63 months, and the recurrence rate of the pCR group (2.04%; 95% CI = 0.29-13.62) was lower than the non-pCR group (14.62%; 95% CI = 10.12-20.87). Higher histological grade was also associated high pCR rate (52.0% vs 40.0%). CONCLUSION: The use of PLD-containing chemotherapeutics in neoadjuvant settings might have benefits for non-metastatic operable breast cancer in Taiwanese females.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".