Observational Research in Childhood Infectious Diseases (ORChID): a dynamic birth cohort study
Bibliographic record
Abstract
INTRODUCTION: Even in developed economies infectious diseases remain the most common cause of illness in early childhood. Our current understanding of the epidemiology of these infections is limited by reliance on data from decades ago performed using low-sensitivity laboratory methods, and recent studies reporting severe, hospital-managed disease. METHODS AND ANALYSIS: The Observational Research in Childhood Infectious Diseases (ORChID) study is an ongoing study enrolling a dynamic birth cohort to document the community-based epidemiology of viral respiratory and gastrointestinal infections in early childhood. Women are recruited antenatally, and their healthy newborn is followed for the first 2 years of life. Parents keep a daily symptom diary for the study child, collect a weekly anterior nose swab and dirty nappy swab and complete a burden diary when a child meets pre-defined illness criteria. Specimens will be tested for a wide range of viruses by real-time PCR assays. Primary analyses involves calculating incidence rates for acute respiratory illness (ARI) and acute gastroenteritis (AGE) for the cohort by age and seasonality. Control material from children when they are without symptoms will allow us to determine what proportion of ARIs and AGE can be attributed to specific pathogens. Secondary analyses will assess the incidence and shedding duration of specific respiratory and gastrointestinal pathogens. ETHICS AND DISSEMINATION: This study is approved by The Human Research Ethics Committees of the Children's Health Queensland Hospital and Health Service, the Royal Brisbane and Women's Hospital and The University of Queensland. TRIAL REGISTRATION: clinicaltrials.gov NCT01304914.
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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 | 0.011 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".