A virtual care program for outpatients diagnosed with COVID-19: a feasibility study
Bibliographic record
Abstract
BACKGROUND: In patients who are discharged home to self-isolate while coronavirus disease 2019 (COVID-19) test results are pending, there is no formal method for physician assessments or counselling to occur if the result returns positive. Our aim was to develop and test the feasibility of a virtual care program for self-isolating outpatients diagnosed with COVID-19. METHODS: In preparation for this gap in health care, the COVID-19 Expansion to Outpatients (COVIDEO) program was developed at the Sunnybrook Health Sciences Centre, Toronto, Ontario, to provide ongoing care for outpatients diagnosed with COVID-19. As part of a feasibility study, we describe our experiences with the first 50 patients managed using this program from its inception (Mar. 1, 2020) until Mar. 27, 2020. RESULTS: All 50 people who tested positive for COVID-19 at the Sunnybrook Health Sciences Centre and were discharged home to self-isolation during the study period were assessed through the COVIDEO program. Thirty-two patients (64%) were assessed via the Ontario Telemedicine Network virtual care platform, and the remainder by telephone. The median time from viral swab collection to first COVIDEO program assessment was 2 (interquartile range [IQR] 1-2) days. Among the 26 patients for whom further follow-up care through the COVIDEO program was discontinued by the end of March 2020, the median duration of virtual care was 12.5 (IQR 8.75-16) days. During the study period, 6 patients required transfer to hospital for assessment, of whom 4 required admission. INTERPRETATION: We have shown that a virtual care program can be used in the management of outpatients diagnosed with COVID-19. Further studies evaluating its sustainability and impact on health outcomes are underway.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".