World Health Organization Danger Signs to predict bacterial sepsis in newborns: A pragmatic prospective cohort study
Bibliographic record
Abstract
Abstract Background The World Health Organization (WHO) has developed danger signs (DS) to help front-line health workers triage interventions in children with severe illnesses. Our objective was to evaluate the extent to which DS predict bacterial sepsis in young infants presenting with acute illness. Methodology/Principal Findings This prospective study evaluated nine DS in infants younger than 3 months with suspected sepsis in a large regional hospital in Lilongwe, Malawi, between June 2018 and April 2020. The main outcomes were positive blood or cerebrospinal fluid (CSF) cultures and mortality. Blood (n=85/401) and CSF (n=2/204) cultures were positive in 21.2% and 1% of infants, respectively (N=401; gestational age mean ± SD: 37.1±3.3 weeks, birth weight 2865±785 grams). In-hospital deaths occurred in 9.7% (N=39/401) of infants (61.5% within 48h of admission). In univariate analyses, all DS were associated with mortality except for temperature instability and tachypnea, whereas “infant was unable to feed” was the only DS significantly associated with bacterial sepsis. After co-variable adjustments, number of DS predicted mortality (OR: 1.75; 95%CI: 1.43–2.16; p<0.001; AUC-ROC: 0.756) but not positive cultures (OR 1.08; 95%CI: 0.92–1.30; p=0.336). Whether potential bacterial contaminants were included or not did not change results meaningfully. Conclusion/Significance DS predicted fatal outcomes but not positive cultures in a large regional hospital setting. These data imply that the incidence of bacterial sepsis and attributable mortality are unlikely to be accurate based on clinical signs alone, in infants in LMIC settings.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".