Interpreting COVID-19 and Virtual Care Trends: Cohort Study
Bibliographic record
Abstract
BACKGROUND: The coronavirus disease (COVID-19) pandemic is rapidly spreading across the world. As of March 26, 2020, there are more than 500,000 cases and more than 25,000 deaths related to COVID-19, and the numbers are increasing by the hour. OBJECTIVE: The aim of this study was to explore the trends in confirmed COVID-19 cases in North Carolina, and to understand patterns in virtual visits related to symptoms of COVID-19. METHODS: We conducted a cohort study of confirmed COVID-19 cases and patients using an on-demand, statewide virtual urgent care center. We collected data from February 1, 2020, to March 15, 2020. Institutional Review Board exemption was obtained prior to the study. RESULTS: As of March, 18 2020, there were 92 confirmed COVID-19 cases and 733 total virtual visits. Of the total visits, 257 (35.1%) were related to COVID-19-like symptoms. Of the COVID-19-like visits, the number of females was 178 (69.2%). People in the age groups of 30-39 years (n=67, 26.1%) and 40-49 years (n=64, 24.9%) were half of the total patients. Additionally, approximately 96.9% (n=249) of the COVID-like encounters came from within the state of North Carolina. Our study shows that virtual care can provide efficient triaging in the counties with the highest number of COVID-19 cases. We also confirmed that the largest spread of the disease occurs in areas with a high population density as well as in areas with major airports. CONCLUSIONS: The use of virtual care presents promising potential in the fight against COVID-19. Virtual care is capable of reducing emergency room visits, conserving health care resources, and avoiding the spread of COVID-19 by treating patients remotely. We call for further adoption of virtual care by health systems across the United States and the world during the COVID-19 pandemic.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".