A landscape review of the published research output relating to respiratory syncytial virus (RSV) in North & Central America and Europe between 2011-2015
Bibliographic record
Abstract
BACKGROUND: The high disease burden of respiratory syncytial virus (RSV) infection and renewed focus on developing a vaccine has led to sustained interest in published RSV-related research. The majority of this research comes from Europe and North/Central America and this landscape review aimed to identify and characterize RSV-related research published during 2011-2015 in these geographical areas. METHODS: We conducted a literature review on electronic databases Scopus and Web of Science to identify published studies investigating RSV throughout Europe and North/Central America. We stratified RSV-related publications between 2011-2015 by study type, country, research institution and funding body. RESULTS: The annual published output of RSV-related research has increased by 29% over the period 2011-2015. Eighty seven percent (13/15) of the most highly cited papers on RSV during this period were from North America. US universities with the highest number of RSV-related publications included Emory (n = 23), Vanderbilt (n = 23), University of Michigan (n = 21) and Ohio State (n = 20). The UK (n = 125), Netherlands (n = 97) and Spain (n = 76) were major European contributors to RSV-related publications. University Medical Centre Utrecht (n = 40) and Imperial College London (n = 28) were the European universities with the largest number of RSV-related publications. The National Institutes of Health provided funding for one quarter of all RSV-related publications. However, few countries in Eastern Europe, Central America and the Caribbean published RSV-related research. Few epidemiological studies focused on adult populations over 18 years old (n = 28, 7%) with only five publications specifically investigating elderly populations over 65. CONCLUSIONS: This review identifies key regions and research institutions which contributed to RSV-related research during 2011-2015 as well as the donor agencies which supported this research. Further research investment is required in a number of countries. More research in the elderly and in high-risk adults is required given the lack of studies pertaining to these populations. Researchers and those commissioning research can use the data from this review to identify productive research institutions and geographical gaps in RSV research.
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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 | 0.008 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.052 | 0.051 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".