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
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.018 | 0.494 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.047 | 0.003 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.005 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| 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 it