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Record W4411165952 · doi:10.33321/cdi.2017.41.37

Paediatric Active Enhanced Disease Surveillance (PAEDS) annual report 2015: Prospective hospital-based surveillance for serious paediatric conditions

2017· article· en· W4411165952 on OpenAlexaff
Jocelynne McRae, Helen Quinn, Kristine Macartney, PAEDS network

Bibliographic record

VenueCommunicable Diseases Intelligence · 2017
Typearticle
Languageen
FieldImmunology and Microbiology
TopicBacterial Infections and Vaccines
Canadian institutionsChild, Adolescent and Family Mental Health
FundersMarie Bashir Institute, University of SydneySydney Medical SchoolNational Health and Medical Research CouncilDepartment of Health and Aged Care, Australian GovernmentAustralian Government
KeywordsMedicineDisease surveillancePediatricsDisease monitoringProspective cohort studyDiseaseEmergency medicineMedical emergencySurgeryInternal medicine

Abstract

fetched live from OpenAlex

INTRODUCTION: The Paediatric Active Enhanced Disease Surveillance (PAEDS) network is a hospital-based active surveillance system employing prospective case ascertainment for selected serious childhood conditions, particularly vaccine preventable diseases and potential adverse events following immunisation (AEFI). PAEDS data is used to better understand these conditions, inform policy and practice under the National Immunisation Program, and enable rapid public health responses for certain conditions of public health importance. PAEDS enhances data available from other Australian surveillance systems by providing prospective, detailed clinical and laboratory information on children with selected conditions. This is the second of the planned annual PAEDS reporting series, and presents surveillance data for 2015. METHODS: Specialist surveillance nurses screened hospital admissions, emergency department records, laboratory and other data, on a daily basis in 5 paediatric tertiary referral hospitals in New South Wales, Victoria, South Australia, Western Australia and Queensland to identify children with the selected conditions. Standardised protocols and case definitions were used across all sites. Conditions under surveillance in 2015 included acute flaccid paralysis (a syndrome associated with poliovirus infection), acute childhood encephalitis (ACE), influenza, intussusception (IS; a potential AEFI with rotavirus vaccines), pertussis and varicella-zoster virus infection (varicella and herpes zoster). Most protocols restrict eligibility to hospitalisations, ED only presentations are also included for some conditions. METHODS: : In 2015, there were 674 cases identified across all conditions under surveillance. Key outcomes of PAEDS included: contribution to national AFP surveillance to reach WHO reporting targets; identification of signals for Mycoplasma pneumoniae and parechovirus-related outbreaks (ACE surveillance); and demonstration of high influenza activity with vaccine effectiveness (VE) analysis supportive of vaccination. Surveillance for IS remains ongoing with any identified AEFIs reported to the relevant State Health Department; varicella and herpes zoster case numbers decreased slightly from previous years in older children not eligible for catch-up. Pertussis case numbers increased in early 2015 and analysis of cases in children aged <1 year demonstrated the importance of timely childhood and maternal immunisation. CONCLUSIONS: PAEDS continues to provide unique policy-relevant data on serious paediatric conditions using hospital-based sentinel surveillance.

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)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.065
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.000
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 head0.012
GPT teacher head0.299
Teacher spread0.287 · 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.

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

Citations0
Published2017
Admission routes1
Has abstractyes

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