Clinical learnings from a virtual primary care program monitoring mild to moderate COVID-19 patients at home
Bibliographic record
Abstract
BACKGROUND: Virtual consults have replaced in-person visits for many home-isolated patients with COVID-19 disease. OBJECTIVES: To describe the natural history, clinical management and outcomes of community-dwelling patients with COVID-19, who received support from a family medicine-led, virtual CovidCare@Home program in Toronto, Ontario, Canada. METHODS: Observational, descriptive study conducted by retrospective chart review of 98 patients enrolled during the first 5 weeks of program implementation (8 April-11 May 2020); 73 patients with laboratory-confirmed COVID-19, with symptom onset ≤ 14 days before initial consult were included for analysis. Patients were classified as mild, moderate or severe based on WHO Criteria. RESULTS: All patients in the program experienced mild (88%) or moderate (12.3%) disease. No patients were hospitalized or died. Patients were mainly female (70%); with mean age of 43.3 years. Most patients (82.2%) worked in higher risk, healthcare settings. Almost 40% had no medical co-morbidities. Common symptoms were cough (65.8%), fatigue (60.3%), headache (42.5%) and myalgia (39.7%), followed by fever (32.9%), sore throat (21.9%), nasal congestion (21.9%) and rhinorrhea (20.5%). Headache (51%) and anosmia (45.1%) were common among females; fever and breathlessness among males (40.9%). Nine patients (12.3%) experienced worsening of symptoms (mainly respiratory) or exacerbation of co-morbidities, which required care outside the virtual service. CONCLUSION: Patients with mild to moderate COVID-19 disease can be managed safely and effectively in a family medicine-led virtual program. Some sex differences in symptoms were observed. Future work should focus on long-term follow up in view of the existence of so-called 'long-haulers'.
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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 | 0.000 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".