Cardiovascular Safety in Oncology Clinical Trials
Bibliographic record
Abstract
The development of novel treatments has improved cancer outcomes but may result in cardiovascular toxicities. Traditional approaches to clinical trial safety evaluation have limitations in their ability to detect signals of cardiovascular risk. Mechanisms to increase power and specificity to clarify cardiovascular safety are required. However, implications include increased costs and slower development. The Cardiovascular Safety Research Consortium facilitated stakeholder discussions with representation from academia, industry, and regulators. A think tank was assembled with the aim of providing recommendations for improved collection and reporting of cardiovascular safety signals in oncology trials. Two working groups were formed. The first focuses on incorporation of consensus definitions of cardiovascular disease into the Common Terminology Criteria for Adverse Events used in oncology trial reporting. The second group considers methods for ascertainment and adjudication of cardiovascular events in cancer trials. The overarching aim of this primer is to improve understanding of the potential cardiovascular toxicities of cancer therapies. • Improved cardiovascular event definitions should be used in cancer therapy trials. • Refinements in safety data ascertainment methods should allow better characterization of potential treatment-associated toxicities. • Optimal cardiovascular event ascertainment and recording methods may vary depending on drug, development stage, and patient factors.
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 | 0.059 | 0.130 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".