Global PARITY: Study Design for a Multi-Centered, International Point Prevalence Study to Estimate the Burden of Pediatric Acute Critical Illness in Resource-Limited Settings
Bibliographic record
Abstract
BACKGROUND: The burden of pediatric critical illness and resource utilization by children with critical illness in resource limited settings (RLS) are largely unknown. Without specific data that captures key aspects of critical illness, disease presentation, and resource utilization for pediatric populations in RLS, development of a contextual framework for appropriate, evidence-based interventions to guide allocation of limited but available resources is challenging. We present this methods paper which describes our efforts to determine the prevalence, etiology, hospital outcomes, and resource utilization associated with pediatric acute, critical illness in RLS globally. METHODS: We will conduct a prospective, observational, multicenter, multinational point prevalence study in sixty-one participating RLS hospitals from North, Central and South America, Africa, Middle East and South Asia with four sampling time points over a 12-month period. Children aged 29 days to 14 years evaluated for acute illness or injury in an emergency department) or directly admitted to an inpatient unit will be enrolled and followed for hospital outcomes and resource utilization for the first seven days of hospitalization. The primary outcome will be prevalence of acute critical illness, which Global PARITY has defined as death within 48 hours of presentation to the hospital, including ED mortality; or admission/transfer to an HDU or ICU; or transfer to another institution for a higher level-of-care; or receiving critical care-level interventions (vasopressor infusion, invasive mechanical ventilation, non-invasive mechanical ventilation) regardless of location in the hospital, among children presenting to the hospital. Secondary outcomes include etiology of critical illness, in-hospital mortality, cause of death, resource utilization, length of hospital stay, and change in neurocognitive status. Data will be managed via REDCap, aggregated, and analyzed across sites. DISCUSSION: This study is expected to address the current gap in understanding of the burden, etiology, resource utilization and outcomes associated with pediatric acute and critical illness in RLS. These data are crucial to inform future research and clinical management decisions and to improve global pediatric hospital outcomes.
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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 | 0.033 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".