Defining the Incidence and Associated Morbidity and Mortality of Severe Respiratory Syncytial Virus Infection Among Children with Chronic Diseases
Bibliographic record
Abstract
INTRODUCTION: REGAL (RSV Evidence-a Geographical Archive of the Literature) has provided a comprehensive review of the published evidence in the field of respiratory syncytial virus (RSV) in Western countries over the last 20 years. This review covers the risk and burden of RSV infection in children with underlying medical conditions or chronic diseases (excluding prematurity and congenital heart disease). METHODS: A systematic review of publications between January 1, 1995 and December 31, 2015 across PubMed, Embase, The Cochrane Library, and Clinicaltrials.gov was supplemented by papers identified by the authors through March 2017. Studies reporting data for hospital visits/admissions for RSV infection as well as studies reporting RSV-associated morbidity and mortality were included. Study quality and strength of evidence (SOE) were graded. RESULTS: A total of 2703 studies were identified and 58 were included. Down syndrome, irrespective of prematurity and congenital heart disease (moderate SOE), immunocompromised children (low SOE), cystic fibrosis (low SOE), and neurologic conditions (low SOE) were associated with a significantly increased risk of RSV hospitalization. A number of other congenital malformations and chronic conditions were also associated with severe RSV disease (low SOE). In general, pre-existing disease was also a predisposing factor for RSV-related mortality (low SOE). CONCLUSION: Severe RSV infection in infants and young children with underlying medical conditions or chronic diseases poses a significant health burden. Further studies are needed to fully quantify the epidemiology, burden and outcomes in these populations, in particular RSV-attributable 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 | 0.011 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".