Demographic and Clinical Factors Affecting Pediatric Survival in South Kivu, the Democratic Republic of the Congo
Bibliographic record
Abstract
Promoting children's health is challenging in underresourced regions, with worse outcomes in areas of sociopolitical instabilities. This encapsulates the difficulties faced by the Panzi General Referral Hospital (PGRH) in South Kivu, the Democratic Republic of the Congo. In this retrospective, cross-sectional study of 456 children ≤ 18 years who presented to the pediatric emergency department of PGRH between December 2018 and May 2019, we present demographic and clinical predictors that affect pediatric survival. We note that referrals from external clinics (odds ratio [OR], 0.37; 95% CI, 0.18-0.75), poor maternal education (OR, 0.21; 95% CI, 0.07-0.67), diagnoses of meningitis (OR, 0.37; 95% CI, 0.18-0.75) or malnutrition (OR, 0.21; 95% CI, 0.07-0.67) are risk factors hindering pediatric survival. Paternal unemployment or longer durations of hospital stay, on the other hand, are protective toward survival. These predictors confirm the importance of accessibility and availability of medical resources and knowledge as levers to establish an effective, robust network of pediatric care delivery capable of withstanding South Kivu's unresolved political tumult.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".