Systematic review of the global epidemiology, clinical and laboratory profile of enteric fever
Bibliographic record
Abstract
BACKGROUND: Children suffer the highest burden of enteric fever among populations in South Asian countries. The clinical features are non-specific, vary in populations, and are often difficult to distinguish clinically from other febrile illnesses, leading to delayed or inappropriate diagnosis and treatment. We undertook a systematic review to assess the clinical profile and laboratory features of enteric fever across age groups, economic regions, level of care and antibiotic susceptibility patterns. METHODS: We searched PubMed (January 1964-December 2013) for studies describing clinical features in defined cohorts of patients over varying time periods. Studies with all culture-confirmed cases or those with at least 50% culture-confirmed cases were included. 242 reports were screened out of 4398 relevant articles and 180 reports were included for final review. RESULTS: 96% of studies were from an urban location, 96% were hospital-based studies, with 41% of studies were from South Asia. Common clinical features in hospitalized children include high-grade fever, coated tongue, anaemia, nausea/vomiting, diarrhea, constipation, hepatomegaly, splenomegaly neutrophilia, abdominal distension and GI bleeding. In adults' nausea/vomiting, thrombocytopenia and GI perforation predominate. The case-fatality rate in children under 5 years is higher than school aged children and adolescents, and is highest in Sub Saharan Africa and North Africa/Middle East regions. Multi-drug resistant enteric fever has higher rates of complications than drug sensitive enteric fever, but case fatality rates were comparable in both. CONCLUSIONS: Our findings indicate variability in disease presentation in adults compared to children, in different regions and in resistant vs sensitive cases. Majority of studies are from hospitalized cases, and are not disaggregated by age. Despite higher complications in MDR enteric fever, case fatality rate is comparable to sensitive cases, with an overall hospital based CFR of 2%, which is similar to recent global estimates. This review underscores the importance of further epidemiological studies in community settings among children and adults, and the need for further preventable measures to curtail the burden of disease.
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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 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.005 |
| Bibliometrics | 0.016 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".