Detailed phenotyping reveals distinct trajectories of cardiovascular function and symptoms with exposure to modern breast cancer therapy
Bibliographic record
Abstract
BACKGROUND: Breast cancer therapies are associated with a risk of cardiac dysfunction, most commonly defined by changes in left ventricular ejection fraction (LVEF). Recently, the authors identified 3 classes of LVEF change after exposure to anthracyclines and/or trastuzumab using latent class growth modeling. The objective of the current study was to characterize the clinical, biochemical, and functional profiles associated with LVEF trajectory class membership. METHODS: Transthoracic echocardiography and biomarker assessments were performed and questionnaires were administered at standardized intervals in a longitudinal cohort of 314 patients with breast cancer who were treated with anthracyclines and/or trastuzumab. Univariable and multivariable multinomial regression analyses evaluated associations between baseline variables and LVEF trajectory class membership. Generalized estimating equations were used to define mean changes in cardiovascular measures over time within each class. RESULTS: Among the 3 distinct subgroups of LVEF changes identified (stable [class 1]; modest, persistent decline [class 2]; and significant early decline followed by partial recovery [class 3]), higher baseline LVEF, radiotherapy, and sequential therapy with anthracyclines and/or trastuzumab were associated with class 2 or 3 membership. Sustained abnormalities in longitudinal strain and N-terminal pro-B-type natriuretic peptide (NT-proBNP) were observed in patients in class 2, as were heart failure symptoms. Similar abnormalities were observed in patients in class 3, but there was a trend toward recovery, particularly for longitudinal strain. CONCLUSIONS: Patients with modest, persistent LVEF declines experienced sustained abnormalities in imaging and biochemical markers of cardiac function and heart failure symptoms. Further investigation is needed to characterize the long-term risk of heart failure, particularly in those with modest LVEF declines.
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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 | 0.000 | 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 | 0.000 | 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".