Virtual Care During the COVID-19 Pandemic for Patients With Hematologic Malignancies: A Single-Institution Experience
Bibliographic record
Abstract
PURPOSE: The use of virtual care rapidly increased during the COVID-19 pandemic and has persisted as a routine method of care delivery. Much of the literature on virtual care in oncology has focused on solid tumors, and little is known about its application in malignant hematology. METHODS: We performed a retrospective review of patients with hematologic malignancies at Princess Margaret Cancer Centre from October 2019 to March 2021 to determine the use of virtual care during this period, cost-savings associated with virtual visits, and patient satisfaction. Patient satisfaction was assessed using the Your Voice Matters survey, a provincially administered survey to evaluate patient experience. RESULTS: Overall, 12.1% (1,122/9,295) of patients had a virtual visit during the study period (0% from October 2019 to February 2020, 36% from March to August 2020, and 30% from September 2020 to March 2021), of which 36% were in the lymphoma clinic and 46% were in the myeloma clinic. The mean two-way opportunity cost for an in-person visit was $168.00 CAD per person with public transit, and $120.40 CAD per person driving. Responses to the Your Voice Matters survey indicated that patients with a virtual visit reported that physical symptoms were discussed appropriately (mean 4.73/5), and were more likely to ask for a follow-up virtual visit compared with patients with in-person visits (mean 4.50/5 v 3.02/5, respectively; P < .01). CONCLUSION: These findings suggest that virtual care may be a feasible and well-received tool for delivering care to a substantial proportion of patients with hematologic malignancies, while enabling substantial cost-savings to patients.
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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 | 0.001 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".