Clinical manifestations of COVID‐19: An overview of 102 systematic reviews with evidence mapping
Bibliographic record
Abstract
OBJECTIVE: Coronavirus disease 2019 (COVID-19) has rapidly spread worldwide, but there is so far no comprehensive analysis of all known symptoms of the disease. Our study aimed to present a comprehensive picture of the clinical symptoms of COVID-19 using an evidence map. METHODS: We systematically searched MEDLINE via PubMed, Web of Science, Embase, and Cochrane library from their inception to March 16, 2021. We included systematic reviews reporting the clinical manifestations of COVID-19 patients. We followed the PRISMA guidelines, and the study selection, data extraction, and quality assessment were done by two individuals independently. We assessed the methodological quality of the studies using AMSTAR. We visually presented the clinical symptoms of COVID-19 and their prevalence. RESULTS: A total of 102 systematic reviews were included, of which, 68 studies (66.7%) were of high quality, 19 studies (18.6%) of medium quality, and 15 studies (14.7%) of low quality. We identified a total of 74 symptoms including 17 symptoms of the respiratory system, 21 symptoms of the neurological system, 10 symptoms of the gastrointestinal system, 16 cutaneous symptoms, and 10 ocular symptoms. The most common symptoms were fever (67 studies, ranging 16.3%-91.0%, pooled prevalence: 64.6%, 95%CI, 61.3%-67.9%), cough (68 studies, ranging 30.0%-72.2%, pooled prevalence: 53.6%, 95%CI, 52.1%-55.1%), muscle soreness (56 studies, ranging 3.0%-44.0%, pooled prevalence: 18.7%, 95%CI, 16.3%-21.3%), and fatigue (52 studies, ranging 3.3%-58.5%, pooled prevalence: 29.4%, 95%CI, 27.5%-31.3%). The prevalence estimates for COVID-19 symptoms were generally lower in neonates, children and adolescents, and pregnant women than in the general populations. CONCLUSION: At least 74 different clinical manifestations are associated with COVID-19. Fever, cough, muscle soreness, and fatigue are the most common, but attention should also be paid to the rare symptoms that can help in the early diagnosis of the disease.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Meta-epidemiology (broad)Metaresearch Domain: Methods · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Systematic review | low |
| gpt | no category Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Systematic review | medium |
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 | 0.028 | 0.105 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.012 | 0.014 |
| Bibliometrics | 0.032 | 0.029 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".