Prospective validation of pediatric disease severity scores to predict mortality in Ugandan children presenting with malaria and non-malaria febrile illness
Bibliographic record
Abstract
INTRODUCTION: The development of simple clinical tools to identify children at risk of death would enable rapid and rational implementation of lifesaving measures to reduce childhood mortality globally. METHODS: We evaluated the ability of three clinical scoring systems to predict in-hospital mortality in a prospective observational study of Ugandan children with fever. We computed the Lambaréné Organ Dysfunction Score (LODS), Signs of Inflammation in Children that Kill (SICK), and the Pediatric Early Death Index for Africa (PEDIA). Model discrimination was evaluated by comparing areas under receiver operating characteristic curves (AUCs) and calibration was assessed using the Hosmer-Lemeshow goodness-of-fit test. Sub-analyses were performed in malaria versus non-malaria febrile illness (NMFI), and in early (≤48 hours) versus late (>48 hours) deaths. RESULTS: In total, 2089 children with known outcomes were included in the study (99 deaths, 4.7% mortality). All three scoring systems yielded good discrimination (AUCs, 95% confidence interval (CI): LODS, 0.90, 0.88 to 0.91; SICK, 0.85, 0.83 to 0.86; PEDIA, 0.90, 0.88 to 0.91). Using the Youden index to identify the best cut-offs, LODS had the highest positive likelihood ratio (+LR, 95% CI: LODS, 6.5, 5.6 to 7.6; SICK, 4.4, 3.9 to 5.0; PEDIA, 4.4, 3.9 to 5.0), whereas PEDIA had the lowest negative likelihood ratio (-LR, 95% CI: LODS, 0.21, 0.1 to 0.3; SICK, 0.22, 0.1 to 0.3; PEDIA, 0.16, 0.1 to 0.3), LODS and PEDIA were well calibrated (P = 0.79 and P = 0.21 respectively), and had higher AUCs than SICK in discriminating between survivors and non-survivors in malaria (AUCs, 95% CI: LODS, 0.92, 0.90 to 0.93; SICK, 0.86, 0.84 to 0.87; PEDIA, 0.92, 0.90 to 0.93), but comparable AUCs in NMFI (AUCs, 95% CI: LODS, 0.86, 0.83 to 0.89; SICK, 0.82, 0.79 to 0.86; PEDIA, 0.87, 0.83 to 0.893). The majority of deaths in the study occurred early (n = 85, 85.9%) where LODS and PEDIA had good discrimination. CONCLUSIONS: All three scoring systems predicted outcome, but LODS holds the most promise as a clinical prognostic score based on its simplicity to compute, requirement for no equipment, and good discrimination.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".