Paediatric Virology and its interaction between basic science and clinical practice (Review)
Bibliographic record
Abstract
The 3rd Workshop on Paediatric Virology, which took place on October 7th, 2017 in Athens, Greece, highlighted the role of breast feeding in the prevention of viral infections during the first years of life. Moreover, it focused on the long-term outcomes of respiratory syncytial virus and rhinovirus infections in prematurely born infants and emphasised the necessity for the development of relevant preventative strategies. Other topics that were covered included the vaccination policy in relation to the migration crisis, mother‑to‑child transmission of hepatitis B and C viruses, vaccination against human papilloma viruses in boys and advances on intranasal live‑attenuated vaccination against influenza. Emphasis was also given to the role of probiotics in the management of viral infections in childhood, the potential association between viral infections and the pathogenesis of asthma, fetal and neonatal brain imaging and the paediatric intensive care of children with central nervous system viral infections. Moreover, an interesting overview of the viral causes of perinatal mortality in ancient Greece was given, where recent archaeological findings from the Athenian Agora's bone well were presented. Finally, different continuing medical educational options in Paediatric Virology were analysed and evaluated. The present review provides an update of the key topics discussed during the workshop.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".