The Clinical Efficacy and Safety of Paclitaxel in Adjuvant Therapy of Breast Cancer: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Purpose: To systematically review the clinical efficacy and adverse reactions of Paclitaxel for the treatment of mammary cancer. Math: We searched Web of knowledge, PubMed, VIP information and CNKI (to October 2013) on randomised controlled trial about Paclitaxel for the treatment of mammary cancer and retrieved relevant reference and research material by hand. Two authors independently screened document, extracted data and assessed the quality according to inclusion and exclusion criteria, we finally used the software RevMan 5.2 from Cochrane for Meta-analysis. Result: 18 randomized controlled clinical study were brought into our study according to inclusion and exclusion criteria, including 10712 patients. The result of meta-analysis showed that the odds ratios of Paclitaxel for adjunctive therapy [OR = 1.64, 95% CI (1.40, 1.92), P <0.00001] was better than conventional drugs, while the overall survival was no significant difference between Paclitaxel and conventional drugs. The further Subgroup analysis showed that the efficacy of Paclitaxel for adjunctive therapy was better than cyclophosphamide [OR = 1.41, 95%C I (1. 07, 1.85), P =0. 01] and NVB [OR = 2.10, 95% CI (1.33, 3.30), P =0.001]. The adverse reactions analysis results showed the ratio of myelosuppression and alopecia by treated with Paclitaxel was improved, while the occurrence of gastrointestinal reaction rate was decreased. Conclusion: The current evidence showed Paclitaxel was effective for the adjuvant treatment of breast cancer, but the above conclusions still need future expansion of more samples, high quality RCT verify. Keyword: Paclitaxel, Breast cancer, efficacy, adverse reactions, systematically review, Meta-analysis, randomized controlled trial.
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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 | 0.014 | 0.031 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.031 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".