PP069 Topic: AS09–Global Health/Resource Limited Setting/Health Inequalities/Impact of Global Warming/Other: DEVELOPMENT OF A NOVEL PEDIATRIC CRITICAL CARE RESOURCE EVALUATION (CRITCARE) TOOL FOR HOSPITALS IN RESOURCE VARIABLE SETTINGS
Bibliographic record
Abstract
Aims & Objectives: Children with acute critical illness in low- and middle-income countries (LMICs) are often managed in hospitals with different capacities for providing critical care. Elements that contribute to critical care capacity can include infrastructure, personnel, resources, processes, education and training. This work outlines the development of a pediatric Critical CAre Resource Evaluation (CritCARE) tool which hospitals across resource-variable settings can use to objectively assess their baseline pediatric critical care capacity and identify opportunities for improvement. Methods: A scoping literature review was performed to identify articles published between 2002-2022 describing existing classification schemes for levels of critical care. A group of interdisciplinary, international experts in pediatric critical care medicine (N=29) organized the elements and themes contained within the classification schemes to create a foundational framework of domains and subdomains for the CritCARE tool. Results: Of 1,947 abstracts screened, 27 studies qualified for concept extraction. The expert group identified 5 levels of pediatric critical care services, including: advanced, comprehensive, general, basic, and none. Categorization of critical care resources by level-of-care resulted in 5 domains and 16 subdomains for the CritCARE framework (Table 1).Conclusions: We propose an evidence-informed framework to define pediatric critical care levels and assess capacity at hospitals in resource-variable settings including LMICs. Next, the CritCARE tool will be piloted across 46 global PARITY (Pediatric Acute Critical Illness Study) centers for usability. This tool represents a starting point to help hospitals and health systems identify gaps, self-classify, benchmark and standardize care based on level of services and local resources. Keywords: Global Health, Pediatric Critical Care Medicine, Intensive Care, Metrics
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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 | 0.017 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.026 | 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".