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Record W2768438595 · doi:10.1111/apa.14148

Virus detection in critically ill children with acute respiratory disease: a new profile in view of new technology

2017· article· en· W2768438595 on OpenAlexafffund
Atsushi Kawaguchi, Angela Bates, Bonita E. Lee, Steven J. Drews, Daniel Garros

Bibliographic record

VenueActa Paediatrica · 2017
Typearticle
Languageen
FieldMedicine
TopicRespiratory viral infections research
Canadian institutionsProvincial Laboratory of Public HealthAlberta Health ServicesUniversity of Alberta HospitalUniversity of Alberta
FundersWomen and Children's Health Research Institute
KeywordsMedicineRhinovirusIntubationEnterovirusRespiratory systemVirusEpidemiologyIntensive care unitDiseaseRespiratory diseaseIntensive care medicineInternal medicineImmunologyLungAnesthesia

Abstract

fetched live from OpenAlex

AIM: To describe the epidemiology of critically ill children admitted to a paediatric intensive care unit (PICU) with acute respiratory disease. The association with intubation was analysed for the three most prevalent viruses and in those with and without viral co-infection. METHODS: Patients admitted to the PICU (2004-2014) with acute respiratory disease were included. Analyses were performed utilising each respiratory viral infection or multiple viral infections as an exposure. RESULTS: There were 1766 admissions with acute respiratory disease of which 1372 had respiratory virus testing and 748 had one or more viruses detected. The risk of intubation before or during the PICU stay was higher if parainfluenza virus was detected compared to respiratory syncytial virus (RSV) (OR: 2.20; 95% CI: 1.06-4.56). Sixty-three admissions had two or more viruses detected, and the combination of RSV and Rhinovirus/enterovirus was the most common. No significant difference was observed in the risk of intubation between patients with multiple and single viral infections. CONCLUSION: Higher risk of intubation was found in patients with parainfluenza as compared to RSV. The risk of intubation comparing parainfluenza virus to other viruses and for patients with multiple versus single virus needs to be further studied.

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)0.000
metaresearch head score (Gemma)0.002
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 score0.148
Threshold uncertainty score0.650

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
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 head0.025
GPT teacher head0.341
Teacher spread0.315 · 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

Citations7
Published2017
Admission routes2
Has abstractyes

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