Clinical features of severe patients infected with 2019 novel coronavirus: 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
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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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 | 0.002 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.018 | 0.003 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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