Paediatric Active Enhanced Disease Surveillance inaugural annual report, 2014
Bibliographic record
Abstract
INTRODUCTION: The Paediatric Active Enhanced Disease Surveillance (PAEDS) network is a hospital-based active surveillance system employing prospective case ascertainment of selected uncommon vaccine preventable diseases and potential adverse events following immunisation (AEFI). PAEDS enhances other Australian surveillance systems by providing prospective detailed clinical and laboratory data for the same child. METHODS: Specialist surveillance nurses screen hospital admissions, emergency department records, laboratory and other data, to prospectively identify hospitalised children aged under 15 years in 5 paediatric tertiary referral hospitals in New South Wales, Victoria, South Australia, Western Australia and Queensland. Standardised protocols and case definitions are used across all sites. Conditions under surveillance include vaccine preventable diseases: acute flaccid paralysis, varicella, pandemic and seasonal influenza and pertussis, and potential AEFIs: febrile seizures and intussusception. PAEDS also conducts surveillance for acute childhood encephalitis. RESULTS: Since August 2007, PAEDS has recruited a total of 6,227 hospitalised cases in total, for all conditions. From January to December 2014, there were 1,220 cases recruited across all conditions. Key outcomes include: enhanced acute flaccid paralysis surveillance to reach World Health Organization targets; supporting varicella and influenza vaccination in children; confirmation of a known low risk of febrile seizures following the 1st dose of measles-mumps-rubella vaccine but no increased risk of febrile seizures after measles-mumps-rubella-varicella vaccine, and a slightly increased risk of developing intussusception 1-7 days after rotavirus vaccination in infants aged less than 3 months. Acute childhood encephalitis data facilitated rapid investigation and response to the enterovirus 71 outbreak in 2013-2014. CONCLUSIONS: PAEDS provides unique policy-relevant data. This is the first of planned PAEDS annual reports to Communicable Diseases Intelligence.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.010 |
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".