Cardio‐oncology care in the era of the coronavirus disease 2019 (COVID‐19) pandemic: An International Cardio‐Oncology Society (ICOS) statement
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has given rise to a pandemic of unprecedented proportions in the modern era because of its highly contagious nature and impact on human health and society: coronavirus disease 2019 (COVID-19). Patients with cardiovascular (CV) risk factors and established CV disease (CVD) are among those initially identified at the highest risk for serious complications, including death. Subsequent studies have pointed out that patients with cancer are also at high risk for a critical disease course. Therefore, the most vulnerable patients are seemingly those with both cancer and CVD, and a careful, unified approach in the evaluation and management of this patient population is especially needed in times of the COVID-19 pandemic. This review provides an overview of the unique implications of the viral outbreak for the field of cardio-oncology and outlines key modifications in the approach to this ever-increasing patient population. These modifications include a shift toward greater utilization of cardiac biomarkers and a more focused CV imaging approach in the broader context of modifications to typical practice pathways. The goal of this strategic adjustment is to minimize the risk of SARS-CoV-2 infection (or other future viral outbreaks) while not becoming negligent of CVD and its important impact on the overall outcomes of patients who are being treated for cancer.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| 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 it