Perspectives on the COVID-19 pandemic impact on cardio-oncology: results from the COVID-19 International Collaborative Network survey
Bibliographic record
Abstract
BACKGROUND: Re-allocation of resources during the COVID-19 pandemic has resulted in delays in care delivery to patients with cardiovascular disease and cancer. The ability of health care providers to provide optimal care in this setting has not been formally evaluated. OBJECTIVES: To assess the impact of COVID-19 resource re-allocation on scheduling, testing, elective procedures, telemedicine access, use of new COVID-19 therapies, and providers' opinions on healthcare policies among oncology and cardiology practitioners. METHODS: An electronic survey was conducted by a cardio-oncology collaborative network through regional and state chapters of the American College of Cardiology, American Society of Clinical Oncology, and the International Cardio-Oncology Society. Descriptive statistics were reported by frequency and proportion for analyses, and stratified categorically by geographic region and specialty. RESULTS: One thousand four hundred fifteen providers (43 countries) participated: 986 cardiologists, 306 oncologists, and 118 trainees/internal medicine. 63% (195/306) of oncologists vs 92% (896/976) of cardiologists reported cancellations of treatments/elective procedures (p = 0.01). 46% (442/970) of cardiologists and 25% (76/303) of oncologists modified the scope of their practice (p = < 0.001). Academic physicians (74.5%) felt better supplied with personal protective equipment (PPE) vs non-academic (74.5% vs 67.2%; p = 0.018). Telemedicine was less common in Europe 81% (74/91), and Latin America 64% (101/158), than the United States, 88% (950/1097) (p = < 0.001). 95% of all groups supported more active leadership from medical professional societies. CONCLUSIONS: These results support initiatives to promote expanded coverage for telemedicine, increased access to PPE, better testing availability and involvement of medical professional societies to help with preparedness for future health care crisis.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".