Identifying Targets for Improving Quality of Pediatric Emergency and Critical Care in Low-Resource Settings
Bibliographic record
Abstract
Introduction: Nearly 6 million children and adolescents died worldwide in 2021, largely due to preventable causes and disproportionately in low- and middle-income countries (LMICs). To reduce child mortality in LMICs, targeted pediatric emergency and critical care (PECC) quality-of-care interventions are needed. Several knowledge gaps must be addressed to inform their design. Objectives: This thesis (1) describes the epidemiology of disease and outcomes of children 0–14 years presenting with acute illness to a District Headquarter (DHQ) hospital in Pakistan, (2) synthesizes the evidence for performance of pediatric mortality prognostic models developed and validated in LMICs, and (3) describes the global infrastructure for PECC in low-resource settings. Synthesis: In Chapter 3, we included 3850 children presenting to Sanghar DHQ hospital outpatient department and 1286 admitted children. Communicable diseases were the most common presenting diagnoses among outpatients and among inpatients 1–9 years. Non-communicable diseases and nutritional disorders were more common with increasing age. At 28 days from hospital admission, 50 children had died. Age <28 days was associated with increased odds of death (OR 4.39 [95% CI 2.4–8.26], p<0.001, reference age: 28 days-14 years). No sex differences in mortality risk were observed. Leading causes of death included neonatal sepsis/meningitis, neonatal encephalopathy, and lower respiratory tract infections. In Chapter 4, we identified seven prognostic models for hospital mortality validated in >1 cohort. The Lambarene Organ Dysfunction Score [0.85 (0.63–0.95)] and Signs of Inflammation in Children that Kill [0.85 (0.84–0.86)] had the highest summary C-statistics (95% CI). All models were at high risk of bias. In Chapter 5, we described infrastructure for PECC in 238 hospitals in 60 countries, predominantly in Latin America and Africa (N=161, 67.4%). Across 174 intensive care units (ICUs), there were statistically significant differences in the proportion of hospitals reporting consistent resource availability between World Bank country income groups. Resources with low availability in low- and lower-middle income countries included specialized staff, ICU equipment, diagnostic tests (imaging, microbiology, biochemistry), and commonly used drugs. Conclusion: This thesis addresses important knowledge gaps regarding the foundations for delivery of high-quality PECC to reduce child mortality from acute illness.
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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 | 0.009 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".