Prevalence and clinical implications of respiratory viruses in asthma during stable disease state and acute attacks: Protocol for a meta-analysis
Bibliographic record
Abstract
INTRODUCTION: Viruses are detected in over 50% of acute asthma attacks and in a notable proportion of patients with asthma during stable disease state They are associated with worse outcomes. We will conduct a series of systematic reviews and meta-analyses to quantify the prevalence and clinical burden of various respiratory viruses in stable asthma and acute asthma attacks. In addition, we will assess the viral loads of respiratory viruses during stable and acute asthma, to explore whether viral load could differentiate attacks triggered by viruses versus those where viruses are present as "innocent bystanders". MATERIALS AND METHODS: Based on a prospectively registered protocol (PROSPERO, ID: CRD42023375108) and following standard methodology recommended by Cochrane, we will systematically search Medline/PubMed, EMBASE, the Cochrane Library and relevant conference proceedings for studies assessing the prevalence or clinical burden of respiratory viruses in asthma. Methodological rigour of the included studies will be appraised using a tool specific for prevalence studies and the Newcastle-Ottawa Scale respectively. In anticipation of significant clinical and methodological heterogeneity, we will conduct random effect meta-analyses. For evaluating the prevalence of viruses, we will perform meta-analyses of proportions using the inverse variance method, and the Freeman-Tukey transformation. We will conduct meta-regression analyses for exploring heterogeneity. CONCLUSION: We envisage that these systematic reviews and meta-analyses will quantify the prevalence and burden of respiratory viruses in stable and acute asthma and will drive future research and clinical practice.
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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 | 0.073 | 0.112 |
| Meta-epidemiology (narrow) | 0.006 | 0.004 |
| Meta-epidemiology (broad) | 0.017 | 0.037 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.052 | 0.006 |
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".