Comparison of influenza type A and B with COVID‐19: A global systematic review and meta‐analysis on clinical, laboratory and radiographic findings
Why this work is in the frame
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Bibliographic record
Abstract
We compared clinical symptoms, laboratory findings, radiographic signs and outcomes of COVID-19 and influenza to identify unique features. Depending on the heterogeneity test, we used either random or fixed-effect models to analyse the appropriateness of the pooled results. Overall, 540 articles included in this study; 75,164 cases of COVID-19 (157 studies), 113,818 influenza type A (251 studies) and 9266 influenza type B patients (47 studies) were included. Runny nose, dyspnoea, sore throat and rhinorrhoea were less frequent symptoms in COVID-19 cases (14%, 15%, 11.5% and 9.5%, respectively) in comparison to influenza type A (70%, 45.5%, 49% and 44.5%, respectively) and type B (74%, 33%, 38% and 49%, respectively). Most of the patients with COVID-19 had abnormal chest radiology (84%, p < 0.001) in comparison to influenza type A (57%, p < 0.001) and B (33%, p < 0.001). The incubation period in COVID-19 (6.4 days estimated) was longer than influenza type A (3.4 days). Likewise, the duration of hospitalization in COVID-19 patients (14 days) was longer than influenza type A (6.5 days) and influenza type B (6.7 days). Case fatality rate of hospitalized patients in COVID-19 (6.5%, p < 0.001), influenza type A (6%, p < 0.001) and influenza type B was 3%(p < 0.001). The results showed that COVID-19 and influenza had many differences in clinical manifestations and radiographic findings. Due to the lack of effective medication or vaccine for COVID-19, timely detection of this viral infection and distinguishing from influenza are very important.
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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 | 0.011 | 0.193 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.029 | 0.001 |
| Bibliometrics | 0.000 | 0.004 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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