Heart failure following oncological treatment
Bibliographic record
Abstract
PURPOSE OF REVIEW: The aim of this review is to give the reader an up-to-date overview of the progress made in the burgeoning field of cardio-oncology, encompassing oncological treatments conferring risk, prediction strategies to identify patients at risk, imaging and biomarker monitoring for emergent or subclinical toxicity and prevention in primary and secondary settings with a focus on heart failure. RECENT FINDINGS: The rapid recent advances in cancer management, particularly with the expansion of targeted and immunotherapies, have led to substantial improvements in outcome, but have also added to the potential causes of cardiac toxicity, which can lead to heart failure. Against this, there has been progression in the field of imaging for cardiac toxicity, identification of at-risk individuals and the clarification of the role of therapy for prevention and treatment of cardiac toxicity. SUMMARY: The findings described in this review provide guidance to clinicians in order to direct monitoring strategy and therapy choice, both in the individual with preexisting cardiac comorbidities and in those predicted to be at the highest risk of cardiac toxicity wherever therapy elements carrying cardiac risk are considered oncologically appropriate.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".