Myocarditis in the Setting of Cancer Therapeutics
Bibliographic record
Abstract
Recent developments in cancer therapeutics have improved outcomes but have also been associated with cardiovascular complications. Therapies harnessing the immune system have been associated with an immune-mediated myocardial injury described as myocarditis. Immune checkpoint inhibitors are one such therapy with an increasing number of case and cohort reports describing a clinical syndrome of immune checkpoint inhibitor–associated myocarditis. Although the full spectrum of immune checkpoint inhibitor–associated cardiovascular disease still needs to be fully defined, described cases of myocarditis range from syndromes with mild signs and symptoms to fatal events. These observations in the clinical setting stand in contrast to outcomes from randomized clinical trials in which myocarditis is a rare event that is investigator reported and lacking in a specific case definition. The complexities associated with diagnosis, as well as the heterogeneous clinical presentation of immune checkpoint inhibitor–associated myocarditis, have made ascertainment and identification of myocarditis with high specificity challenging in clinical trials and other data sets, limiting the ability to better understand the incidence, outcomes, and predictors of these rare events. Therefore, establishing a uniform definition of myocarditis for application in clinical trials of cancer immunotherapies will enable greater understanding of these events. We propose an operational definition of cancer therapy-associated myocarditis that may facilitate case ascertainment and report and therefore may enhance the understanding of the incidence, outcomes, and risk factors of this novel clinical syndrome.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".