Effect of Corticosteroid Therapy on Influenza-Related Mortality: A Systematic Review and Meta-analysis
Bibliographic record
Abstract
BACKGROUND: Most studies have reported that corticosteroid therapy adversely influences influenza-related outcomes. METHODS: Electronic databases were searched from inception to March 2013 for experimental and observational studies investigating systemic corticosteroid therapy for presumed influenza-associated complications. Meta-analysis of Observational Studies in Epidemiology guidelines were adopted. Pooled odds ratios (ORs) and 95% confidence intervals (CIs) were estimated using random-effects models, and heterogeneity was assessed using the I(2) statistic. Quality of evidence was assessed using the Grading Assessment, Development, and Evaluation system. RESULTS: We identified 16 eligible studies (3039 individuals), all of which were observational; 10 (1497 individuals) were included in the meta-analysis of mortality, of which 9 studied patients with 2009 pandemic influenza A virus subtype H1N1. Risk of bias was greatest in the comparability domain of the Newcastle-Ottawa scale, consistent with potential confounding by indication, and data specific to mortality were of low quality. Meta-analysis found an increased odds of mortality (OR, 2.12; 95% CI, 1.36-3.29) associated with corticosteroid therapy. Subgroup analysis of adjusted estimates from 4 studies with very low statistical heterogeneity found a similar association (OR, 2.58; 95% CI, 1.39-4.79). CONCLUSIONS: No completed clinical trials were identified. Evidence from observational studies, with important limitations, suggests that corticosteroid therapy for presumed influenza-associated complications is associated with increased 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 | 0.015 | 0.037 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.040 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".