Respiratory pathogens and clinical outcomes in children with an asthma exacerbation: A systematic review
Bibliographic record
Abstract
Background: In asthmatic children, respiratory pathogens are identified in 60%–80% of asthma exacerbations, contributing to a significant burden of illness. The role of pathogens in the clinical evolution of exacerbations is unknown. Objective: We systematically reviewed the association between the presence of pathogens and clinical outcomes in children with an asthma exacerbation. Method: PubMed, EMBASE, BIOSIS, and the Cochrane Central Register of Controlled Trials were searched up to October 2016 for studies reporting on respiratory pathogen exposure and clinical outcome. The Risk of Bias in Non-Randomized Studies of Interventions tool was used for quality assessment. Results: Twenty-eight observational studies ( N = 4,224 children) reported on 112 different associations between exposure to any pathogen ( n = 45), human rhinovirus (HRV; n = 34), atypical bacteria ( n = 21), specific virus ( n = 11), or bacteria ( n = 1) and outcomes of exacerbation severity ( n = 26), health care use ( n = 38), treatment response ( n = 19), and morbidity ( n = 29). Restricting the analysis only to comparisons with a low to moderate risk of bias, we observed an association between HRV and higher exacerbation severity on presentation (regression p = .016) and between the presence of any pathogen and emergency department treatment failure (odds ratio [OR] = 1.57; 95% CI 1.04% to 2.37%). High-quality evidence for effect on morbidity or health care use is lacking. Conclusions: Further research on the role of pathogen–treatment interaction and outcomes is required to inform the need for point-of-care, real-time testing for pathogens. Studies with a sufficiently large sample size that address selection bias, correctly adjust for confounding, and rigorously report core patient-centred outcomes are necessary to improve knowledge.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".