A Systematic Review on the Risk Factors of Developing Long COVID in Asia Pacific
Bibliographic record
Abstract
Recently, healthcare workers and patients noticed that COVID-19 survivors experienced persistent symptoms after recovering from the acute infection. Due to insufficient research on Long COVID especially in Asia Pacific, this study aims to determine the prevalence of Long COVID and its associations with selected socio-demographic factors (age, gender, BMI, and severity of acute COVID-19) among COVID-19 patients in this region. Articles were searched from several journal databases reporting at least one-month of persistent COVID symptoms. The selection of the studies was based on the PRISMA flow diagram. Newcastle-Ottawa Scale (NOS) was adopted for quality assessment of the articles and sixteen papers were included in this study. The prevalence of Long COVID reported in the studies ranged from 8.2% to 68%. Existing evidence suggested that female gender, older age, severe acute COVID-19 stage, and higher BMI were more likely to develop Long COVID. This study demonstrated a significant portion of the population may be affected with Long COVID, particularly those with a higher risk. Hence, more emphasis on Long COVID should be given to maintain the quality of life among COVID-19 patients. Keywords: long covid, persistent COVID-19 symptoms, post-COVID syndrome, long-term sequelae, risk factors of long covid
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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 | 0.029 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".