Do corticosteroids reduce the mortality of influenza A (H1N1) infection? A 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: Corticosteroids are used empirically in influenza A (H1N1) treatment despite lack of clear evidence for effective treatment. This study aims to assess the efficacy of corticosteroids treatment for H1N1 infection. METHODS: Systematic review and meta-analysis were used to estimate the efficacy of corticosteroids for the prevention of mortality in H1N1 infection. Databases searched included MEDLINE, EMBASE, PubMed, Cochrane Central Register of Controlled Clinical Trials and so on, and bibliographies of retrieved articles, from April 2009 to October 2014. We included both cohort studies and case-control studies reported in English or Chinese that compared treatment effects between corticosteroids and non-corticosteroids therapy in inpatients with H1N1 virus infection. Cohort studies employed mortality as outcome, and case-control studies employed deaths as cases and survivors as controls; both were assessed in this meta-analysis. RESULTS: In total twenty-three eligible studies were included. Both cohort studies (nine studies, n = 1,405) and case-control studies (14 studies, n = 4,700) showed a similar trend toward increased mortality (cohort studies relative risk was 1.85 with 95% confidence interval (CI) 1.46 to 2.33; case-control studies odds ratio was 4.22 with 95% CI 3.10 to 5.76). The results from both subgroup analyses and sensitive analyses were consistent with each other, showing that steroid treatment is associated with mortality. However, considering the fact that corticosteroids were tend to be used in sickest case-patients and heterogeneity was observed between studies, we cannot make a solid conclusion. CONCLUSIONS: Available evidence did not support the use of corticosteroids as standard care for patients with severe influenza. We conclude that further research is required.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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