Systematic Review and Network Meta-Analysis on Treating Hormone Receptor-Positive Metastatic Breast Cancer After CDK4/6 Inhibitors
Bibliographic record
Abstract
INTRODUCTION: The optimal treatment of estrogen receptor-positive (ER +) metastatic breast cancer (MBC) after progression on cyclin-dependent 4/6 kinase inhibitors (CDK4/6i) is unknown. METHODS: We conducted a systematic review and network meta-analysis (NMA) of phase-II/-III randomized trials of ER + MBC post CDK4/6i + ET progression. We calculated the hazard ratio (HR) for progression-free survival (PFS) and overall survival (OS) using generic inverse variance and odds ratios (ORs) using the Mantel-Haenszel method for adverse events (AEs) with Review-Manager version-5.4. NMA was executed using WINBUGS (Microsoft Excel). Three molecular subgroups were analyzed: HER2-low, PI3K/AKT/mTOR, and the ESR1 mutation subgroup for selective estrogen receptor degrader (SERD). RESULTS: A total of 14 studies were included. In the HER2-low group, Sacituzumab govitecan and trastuzumab deruxtecan had a similar efficacy (HR, 95% CI): PFS (0.98; 0.63-1.43) and OS (1.08; 0.76-1.55). In PI3K/AKT/mTOR-altered cases, capivasertib was superior to alpelisib PFS (0.77; 0.53-1.12), and OS (0.80; 0.48-1.35). SERDs had worse PFS and OS versus ongoing CDK 4/6i (ribociclib). CONCLUSION: No therapy emerged as the unequivocal choice in the post-CDK 4/6i domain in unselected subgroups. In the HER2-low population, a similar efficacy and different toxicity spectrum was seen. In AKT-altered tumors, capivasertib was less toxic than alpelisib. PROSPERO ID: CRD4202236412.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.019 | 0.042 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.031 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".