Referral of febrile children in resource-constrained community settings in Asia (Spot Sepsis) – a multi-country, prospective, cohort study
Bibliographic record
Abstract
ABSTRACT In resource-constrained community settings, distinguishing which febrile children require referral is a major unmet need. Current WHO danger signs lack accuracy, resulting in missed severe illness and unnecessary referrals. We developed and validated simple clinical prediction models using data from 3,405 children aged 1-59 months presenting with community-acquired acute febrile illnesses to seven hospitals across Bangladesh, Cambodia, Indonesia, Laos, and Viet Nam. Cambodian data were held-out for external validation. All models outperformed WHO criteria to predict progression to severe febrile illness (death or organ support) within two days (sensitivity=0.56, 95%CI=0.42-0.69; specificity=0.83, 95%CI=0.78-0.87). Incorporating pulse oximetry or the host biomarker sTREM1 further enhanced sensitivity (0.89, 95%CI=0.79-0.97) vs. clinical features alone (0.75, 95%CI=0.62-0.86). The pulse oximetry-based model achieved these gains while improving specificity, concomitantly reducing referral rates three-fold. These approaches appear cost-effective and could transform referral practices for febrile children in resource-constrained community settings. They warrant evaluation in randomised controlled trials.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".