Optimal Choice as First-Line Therapy for Patients with Triple-Negative Breast Cancer: A Bayesian Network Meta-Analysis
Bibliographic record
Abstract
To identify the advantageous therapy as the first-line treatment for patients with triple-negative breast cancer (TNBC). Randomized controlled trials were searched for on Medline, Embase, ClinicalTrials.gov, and the Cochrane Library between January 2001 and December 2021. The primary endpoint was progression-free survival (PFS) and secondary endpoints were overall survival (OS) and treatment-related adverse events (TRAEs). A Bayesian framework was applied to facilitate indirect comparisons, of which the outcomes were presented using cumulative ranking curve (SUCRA) values, synthesized hazard ratio, risk ratio, and 95% credible interval. A total of 3140 patients were identified. Pooled results of PFS revealed that chemotherapy plus AKT inhibitors (AKTi) was likely the most effective therapy among enrolled therapies (SUCRA = 91.6%), of which the result remained consistent in comparative analysis for OS. In addition, no significant difference was detected between PD-1/PD-L1 antibodies in patients, whereas the PD-1 inhibitors (PD-1i) regimen was advantageous over PD-L1 inhibitor (PD-L1i) therapy for PD-L1 positive TNBC. Concerning TRAEs, an apparent heterogeneity associated with safety profiles were denoted among enrolled agents. Chemotherapy plus AKTi was the most effective therapy with comparable safety profiles. Chemotherapy plus the anti-PD-1 regimen was advantageous over the combination therapy based on the PD-L1 blockade.
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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 | 0.025 | 0.040 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.027 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".