Clinical prediction models to diagnose neonatal sepsis in low-income and middle-income countries: a scoping review
Bibliographic record
Abstract
INTRODUCTION: Neonatal sepsis causes significant morbidity and mortality worldwide but is difficult to diagnose clinically. Clinical prediction models (CPMs) could improve diagnostic accuracy, facilitating earlier treatment for cases and avoiding antibiotic overuse. Neonates in low-income and middle-income countries (LMICs) are disproportionately affected by sepsis, yet no review has comprehensively synthesised evidence for CPMs validated in this setting. METHODS: We performed a scoping review of CPMs to diagnose neonatal sepsis using Ovid MEDLINE, Ovid Embase, Scopus, Web of Science, Global Index Medicus and the Cochrane Library. The most recent searches were performed on 16 June 2024. We included studies published in English or Spanish that validated a new or existing CPM for neonatal sepsis in any healthcare setting in an LMIC. Studies were excluded if they validated a prognostic model or where data for neonates could not be separated from a larger paediatric population. Studies were selected by two independent reviewers and summarised by narrative synthesis. RESULTS: From 4598 unique records, we included 82 studies validating 44 distinct models in 24 252 neonates. Most studies were set in neonatal intensive or special care units (n=64, 78%) in middle-income countries (n=81, 99%) and included neonates already suspected of sepsis (n=58, 71%). Only four studies (5%) were set in the WHO African region, and only one study included data from a low-income country. Two-thirds of CPMs (n=30) required laboratory parameters, and three-quarters (n=34) were only validated in one study. CONCLUSION: Our review highlights several literature gaps, particularly a paucity of studies validating models in the lowest-income countries where neonatal sepsis is most prevalent, and models for the undifferentiated neonatal population that do not rely on laboratory tests. Furthermore, heterogeneity in study populations, definitions of sepsis and reporting of models inhibits meaningful comparison between studies and may hinder progress towards useful diagnostic tools.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Systematic review | low |
| gpt | no category Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Other design | high |
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".