Virtual Cancer Care During the COVID-19 Pandemic and Beyond: A Call for Evaluation
Bibliographic record
Abstract
The interplay of virtual care and cancer care in the context of the COVID-19 pandemic is unique and unprecedented. Patients with cancer are at increased risk of SARS-CoV-2 infection and have worse outcomes than patients with COVID-19 who do not have cancer. Virtual care has been introduced quickly and extemporaneously in cancer treatment centers worldwide to maintain COVID-19-free zones. The outbreak of COVID-19 in a cancer center could have devastating consequences. The virtual care intervention that was first used in our cancer center, as well as many others, was a landline telephone in an office or clinic that connected a clinician with a patient. There is a lack of virtual care evaluation from the perspectives of patients and oncology health care providers. A number of factors for assessing oncology care delivered through a virtual care intervention have been described, including patient rapport, frailty, delicate conversations, team-based care, resident education, patient safety, technical effectiveness, privacy, operational effectiveness, and resource utilization. These factors are organized according to the National Quality Forum framework for the assessment of telehealth in oncology. This includes the following 4 domains of assessing outcomes: experience, access to care, effectiveness, and financial impact or cost. In terms of virtual care and oncology, the pandemic has opened the door to change. The lessons learned during the initial period of the pandemic have given rise to opportunities for the evolution of long-term virtual care. The opportunity to evaluate and improve virtual care should be seized upon.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| 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".