Pretreatment Circulating Vascular Biomarkers Predict Cancer Therapy–Related Cardiac Dysfunction During HER2+ Breast Cancer Treatment
Bibliographic record
Abstract
BACKGROUND: Blood biomarkers to predict cancer therapy-related cardiac dysfunction (CTRCD) risk remain limited. OBJECTIVES: breast cancer patients. METHODS: breast cancer receiving anthracycline and trastuzumab therapy underwent serial evaluation with cardiac magnetic resonance imaging (CMR), echocardiography, clinical assessments, and blood biobanking every 3 months. Multiomics profiling of 3 circulating cardiac damage biomarkers and 35 markers of inflammation, angiogenesis and endothelial activation and profiling of >2,000 plasma microRNAs were performed before and early during treatment (3 and 6 months). CTRCD was defined by left ventricular ejection fraction measured on CMR, and sensitivity analyses used echocardiography. Pretreatment protein biomarkers were measured in a validation cohort. RESULTS: breast cancer patients. CONCLUSIONS: Pretreatment endothelial-centric and inflammatory biomarkers outperformed both clinical and CMR measures in predicting CTRCD during chemotherapy.
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 | 0.001 | 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 | 0.001 | 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".