Asymptomatic infection and disappearance of clinical symptoms of COVID-19 infectors in China 2022–2023: a cross-sectional study
Bibliographic record
Abstract
To explore the clinical characteristics of patients infected with SARS-CoV-2 nationwide, especially the effect factors of asymptomatic infection and disappearance of clinical symptoms. A total of 66,448 COVID-19 patients in China who have been diagnosed by nucleic acid test or rapid antigen test were surveyed online (December 24, 2022 to January 16, 2023). Our cross-sectional study used descriptive analyses and binary Logistics regression model to assess the correlation between the clinical characteristics and relative factors, including age, gender, pre-existing conditions, reinfection, vaccination and treatment. A total of 64,515 valid questionnaires were collected. Among included participants, 5969 of which were asymptomatic. The symptoms were mainly upper respiratory symptoms, including dry and itchy throat (64.16%), sore throat (59.95%), hoarseness (57.90%), nasal congestion (53.39%). In binary Logistics regression model, we found that male, no pre-existing conditions, reinfection and vaccination have positive correlations with the appearance of asymptomatic COVID-19 patients. In Cox proportional-hazards regression model, considering all clinical symptoms disappeared in 14 days as outcome, we found that ≤ 60 years old, male, no pre-existing conditions, vaccination and adopted treatment have positive correlations with rapid amelioration of clinical symptoms in COVID-19 patients. The clinical symptoms of the participants were mainly upper respiratory symptoms which were according with the infection of Omicron variant. Factors including age, gender, pre-existing conditions and reinfection could influence the clinical characteristics and prognosis of COVID-19 patients. Importantly, vaccination has positive significance for the prevention and treatment of COVID-19. Lastly, the use of Chinese medicine maybe beneficial to COVID-19 patients, however, reasonable guidance is necessary.
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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".