Comparison of confirmed <scp>COVID</scp>‐19 with <scp>SARS</scp> and <scp>MERS</scp> cases ‐ Clinical characteristics, laboratory findings, radiographic signs and outcomes: A systematic review and meta‐analysis
Bibliographic record
Abstract
INTRODUCTION: Within this large-scale study, we compared clinical symptoms, laboratory findings, radiographic signs, and outcomes of COVID-19, SARS, and MERS to find unique features. METHOD: We searched all relevant literature published up to February 28, 2020. Depending on the heterogeneity test, we used either random or fixed-effect models to analyze the appropriateness of the pooled results. Study has been registered in the PROSPERO database (ID 176106). RESULT: Overall 114 articles included in this study; 52 251 COVID-19 confirmed patients (20 studies), 10 037 SARS (51 studies), and 8139 MERS patients (43 studies) were included. The most common symptom was fever; COVID-19 (85.6%, P < .001), SARS (96%, P < .001), and MERS (74%, P < .001), respectively. Analysis showed that 84% of Covid-19 patients, 86% of SARS patients, and 74.7% of MERS patients had an abnormal chest X-ray. The mortality rate in COVID-19 (5.6%, P < .001) was lower than SARS (13%, P < .001) and MERS (35%, P < .001) between all confirmed patients. CONCLUSIONS: At the time of submission, the mortality rate in COVID-19 confirmed cases is lower than in SARS- and MERS-infected patients. Clinical outcomes and findings would be biased by reporting only confirmed cases, and this should be considered when interpreting the data.
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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 | 0.010 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.027 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".