Infection Surveillance Protocol for a Multicountry Population-based Study in South Asia to Determine the Incidence, Etiology and Risk Factors for Infections Among Young Infants of 0 to 59 Days Old
Bibliographic record
Abstract
BACKGROUND: Insufficient knowledge of the etiology and risk factors for community-acquired neonatal infection in low-income countries is a barrier to designing appropriate intervention strategies for these settings to reduce the burden and treatment of young infant infection. To address these gaps, we are conducting the Aetiology of Neonatal Infection in South Asia (ANISA) study among young infants in Bangladesh, India and Pakistan. The objectives of ANISA are to establish a comprehensive surveillance system for registering newborns in study catchment areas and collecting data on bacterial and viral etiology and associated risk factors for infections among young infants aged 0-59 days. METHODS: We are conducting active surveillance in 1 peri-urban and 4 rural communities. During 2 years of surveillance, we expect to enroll an estimated 66,000 newborns within 7 days of their birth and to follow-up them until 59 days of age. Community health workers visit each young infant in the study area 3 times in the first week of life and once a week thereafter. During these visits, community health workers assess the newborns using a clinical algorithm and refer young infants with signs of suspected infection to health care facilities where study physicians reassess them and provide care if needed. On physician confirmation of suspected infection, blood and respiratory specimens are collected and tested to identify the etiologic agent. CONCLUSIONS: ANISA is one of the largest initiatives ever undertaken to understand the etiology of young infant infection in low-income countries. The data generated from this surveillance will help guide evidence-based decision making to improve health care in similar settings.
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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 | 0.021 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 0.006 |
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".