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Record W2153797869 · doi:10.1155/2013/801581

Increased Risk of RSV Infection in Children with Down's Syndrome: Clinical Implementation of Prophylaxis in the European Union

2013· article· en· W2153797869 on OpenAlexaff
Dianne E.C. van Beek, Bosco Paes, Louis Bont

Bibliographic record

VenueClinical and Developmental Immunology · 2013
Typearticle
Languageen
FieldMedicine
TopicRespiratory viral infections research
Canadian institutionsMcMaster University
FundersAbbott Laboratories
KeywordsMedicineGuidelineBronchiolitisEuropean unionPediatricsRisk factorCohortProspective cohort studyFamily medicineInternal medicineImmunologyVirusPathology

Abstract

fetched live from OpenAlex

Prospective cohort studies show that Down's syndrome (DS) is an independent risk factor for hospitalization for RSV bronchiolitis. It is unknown whether this observation has been translated into specific management for DS children. The primary goal was to assess the knowledge of healthcare providers in the European Union about RSV infection in DS children and to determine whether it influenced the implementation of prophylaxis. DS caregivers were surveyed using a standardized questionnaire, and country-specific guidelines were obtained. Fifty-three caregivers participated. Thirty-nine (86.7%) had knowledge of the increased risk of severe RSV infection in DS children, and 30 (71.4%) graded that it was important to have a statement on the use of RSV prophylaxis in existing guidelines. Twenty-eight participants had a local DS guideline; hard copies of twelve unique guidelines were obtained. Only one (8.3%) contained a statement on RSV prophylaxis for DS, and five considered such a statement for the next version. Conclusion. Most pediatricians had knowledge that DS children have an increased risk of severe RSV infection. Despite the lack of a specific RSV prophylaxis trial in DS, they felt that a statement on RSV prophylaxis in DS guidelines was important, but this was rarely present in current guidelines.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.091
Threshold uncertainty score0.897

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.037
GPT teacher head0.376
Teacher spread0.339 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations18
Published2013
Admission routes1
Has abstractyes

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