Prevention of Anthracyclines and HER2 Inhibitor-Induced Cardiotoxicity: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: This meta-analysis and systematic review aim to consolidate evidence on cardiotoxicity prevention and treatment strategies in patients receiving anthracyclines or HER2 receptor inhibitors, vital treatments for breast cancer and hematologic malignancies. By synthesizing existing research, the goal is to provide impactful insights that enhance patient care and outcomes. METHODS: Comprehensive research across PubMed, Scopus, EMBASE, and the Cochrane Central Register for Controlled Trials was conducted, selecting clinical trials focusing on cardioprotection in anthracyclines or HER2 inhibitor-treated individuals. Effect sizes were computed using OpenMeta (Analyst), with leave-out meta-analysis to assess potential small study effects. Meta-regression explored treatment duration and sample size effects. Evidence quality for primary outcomes was evaluated using ROB, Robins 2, and Newcastle-Ottawa tools. RESULTS: Twenty -three studies involving a total of 14,652 patients (13,221 adults and 1431 kids) were included in the current systematic review and meta-analysis. The risk of bias and methodological quality of the included studies suggested good and moderate quality. Patients prescribed β-blockers demonstrated a 74% lower likelihood of exhibiting cardiotoxicity symptoms (OR 1.736). Similarly, the use of dexrazoxane was linked to a threefold decrease in cardiac abnormalities risk (OR 2.989), and ACE inhibitor administration showed half the risk compared with the control group (OR 1.956). CONCLUSIONS: Through this systematic review and meta-analysis, it was shown that there is a reduction in cardiotoxicity from either anthracyclines or HER2 inhibitors in patients receiving pharmacoprophylaxis.
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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 | 0.018 | 0.038 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.050 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".