Clinical features of severe patients infected with 2019 novel coronavirus: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: 2019 novel coronavirus disease (COVID-19) has posed significant threats to public health. To identify and treat the severe and critical patients with COVID-19 is the key clinical problem to be solved. The present study aimed to evaluate the clinical characteristics of severe and non-severe patients with COVID-19. METHODS: We searched independently studies and retrieved the data that involved the clinical characteristics of severe and non-severe patients with COVID-19 through database searching. Two authors independently retrieved the data from the individual studies, assessed the study quality with Newcastle-Ottawa Scale and analyzed publication bias by Begg's test. We calculated the odds ratio (OR) of groups using fixed or random-effect models. RESULTS: /L and bilateral involvement of chest CT. Severe patents had higher risk on complications including acute cardiac injury (OR 13.48; 95% CI, 3.60 to 50.47, P<0.001) or acute kidney injury (AKI) (OR 11.55; 95% CI, 3.44 to 38.77, P<0.001), acute respiratory distress syndrome (ARDS) (OR 26.12; 95% CI, 11.14 to 61.25, P<0.001), shock (OR 53.17; 95% CI, 12.54 to 225.4, P<0.001) and in-hospital death (OR 45.24; 95% CI, 19.43 to 105.35, P<0.001). Severe group required more main interventions such as received antiviral therapy (OR 1.69; 95% CI, 1.23 to 2.32, P=0.001), corticosteroids (OR 5.07; 95% CI, 3.69 to 6.98, P<0.001), CRRT (OR 37.95; 95% CI, 7.26 to 198.41, P<0.001) and invasive mechanical ventilation (OR 129.35; 95% CI, 25.83 to 647.68, P<0.001). CONCLUSIONS: Severe patients with COVID-19 had more risk of clinical characteristics and multiple system organ complications. Even received more main interventions, severe patients had higher risk of mortality.
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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 | 0.009 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.029 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 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".