Telemedicine and the Use of Korean Medicine for Patients With COVID-19 in South Korea: Observational Study
Bibliographic record
Abstract
BACKGROUND: COVID-19 was first reported in Wuhan, China, in December 2019, and it has since spread worldwide. The Association of Korean Medicine (AKOM) established the COVID-19 telemedicine center of Korean medicine (KM telemedicine center) in Daegu and Seoul. OBJECTIVE: The aim of this study was to describe the results of the KM telemedicine center and the clinical possibility of using herbal medicines for COVID-19. METHODS: All procedures were conducted by voice call following standardized guidelines. The students in the reception group obtained informed consent from participants and they collected basic information. Subsequently, Korean Medicine doctors assessed COVID-19-related symptoms and prescribed the appropriate herbal medicine according to the KM telemedicine guidelines. The data of patients who completed the program by June 30, 2020, were analyzed. RESULTS: From March 9 to June 30, 2020, 2324 patients participated in and completed the KM telemedicine program. Kyung-Ok-Ko (n=2285) was the most prescribed herbal medicine, and Qingfei Paidu decoction (I and II, n=2053) was the second most prescribed. All COVID-19-related symptoms (headache, chills, sputum, dry cough, sore throat, fatigue, muscle pain, rhinorrhea, nasal congestion, dyspnea, chest tightness, diarrhea, and loss of appetite) improved after treatment (P<.001). CONCLUSIONS: The KM telemedicine center has provided medical service to 10.8% of all patients with COVID-19 in South Korea (as of June 30, 2020), and it is still in operation. We hope that this study will help to establish a better health care system to overcome COVID-19.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".