786: THE ETIOLOGY OF HOSPITAL MORTALITY IN CHILDREN IN LMICS: A SYSTEMATIC REVIEW AND META-ANALYSIS
Bibliographic record
Abstract
Introduction: In 2019, 80% of the 7·4million children who died around the world were in low- and middle-income countries (LMICs). This study aimed to determine global and regional estimates of the common causes of pediatric hospital mortality and admission in LMICs and explore regional differences. Methods: This systematic review (PROSPERO #230228) searched MEDLINE, EMBASE, CINAHL, and LILACS to identify observational studies from LMICs published January 1, 2005-February 26, 2021. Eligible studies included a general pediatric (aged >28d-12yrs) admission population, cause of admission or death, and total admissions. We excluded studies with data before 2000 or without a full text. Two, independent reviewers screened and extracted data. We performed a meta-analysis of cause-specific mortality, case fatality rates (CFRs), and cause of admission using random-effects models. We reported proportions as cause of death or cause of admission per 1000 admissions with 95% confidence intervals (CI). Heterogeneity was assessed using the variance estimates. Results: Our search identified 29,637 texts. After duplicate removal, and screening, 257 studies were analysed. The most common causes of mortality (deaths/1000 admissions) were infectious (12 [95%CI 9-14]); respiratory (9 [95%CI 5-13]); and gastrointestinal (9 [95%CI 6-11]). Conditions with the highest CFRs were neurologic (13% [95%CI 9-18%]); cardiovascular (11% [95%CI 6-16%]); and congenital conditions (8% [95%CI 4-12%]). Common causes of admission (cases/1000 admissions) were respiratory (255 [95%CI 231-280]); infectious (214 [95%CI193-234]); and gastrointestinal (166 [95% CI 143-190]). Conclusions: Pediatric hospital mortality is high in LMICs and there are significant regional differences in burden of disease. Global child health efforts must include measures to reduce LMIC hospital mortality including basic emergency and critical care services to address common causes of death. A major priority is supporting LMIC researchers to implement and assess these service-related interventions, measure outcomes, and ensure equity and sustainability.
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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 | 0.015 | 0.035 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.030 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".