Clinical, scientific and healthcare system consequences of misdiagnosing neonatal sepsis
Bibliographic record
Abstract
INTRODUCTION: Neonatal sepsis remains a major contributor to morbidity and mortality worldwide, with the highest burden in low- and middle-income countries (LMICs). Generating accurate estimates of disease burden is critical for setting research priorities, informing health policy, and resource allocation. However, in many LMICs, limited access to timely and reliable diagnostic tools severely limits case detection, undermines epidemiological surveillance, and impedes efforts to improve clinical outcomes. AREAS COVERED: This review examines the clinical, scientific, and health system implications of misdiagnosing neonatal sepsis. We describe the challenges of accurate case identification and summarize findings from prospective, multicenter studies showing marked variability in incidence across different geographic and healthcare settings. We explore the sources of this variability and discuss its impact on patient care, clinical trials interpretation, and progress toward reducing the global burden of neonatal sepsis. EXPERT OPINION: The lack of standardized case definition hinders neonatal sepsis research and may contribute to the growing threat of antimicrobial resistance. Addressing this requires acknowledging the substantial uncertainty in current global incidence estimates. More importantly, it demands shifting focus from passive reporting of variability to actively investigating the methodological, sociodemographic, clinical, biological, and systemic drivers that shape sepsis detection and outcomes across diverse settings.
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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".