Hypoalbuminemia as a predictor of severe dengue: a systematic review and meta-analysis
Bibliographic record
Abstract
INTRODUCTION: Dengue fever is a significant health concern globally, especially in tropical regions. Identifying reliable markers for severe dengue, such as hypoalbuminemia, is crucial for early diagnosis and treatment. METHODS: This review systematically explores the association between hypoalbuminemia and severe dengue. We searched databases including PubMed, Embase, Scopus, Cochrane, and Web of Science until 28 December 2023, focusing on studies that reported albumin levels in dengue patients. Our selection criteria aimed at observational studies, from which data extraction and quality assessment were performed using Nested- Knowledge and the Newcastle-Ottawa Scale. RESULTS: A meta-analysis of 17 studies involving 974 severe and 18,496 non-severe dengue patients identified a standardized mean difference (SMD) in albumin levels of -1.625 g/dL (95% CI: -3.618 to -0.369). Subgroup analysis indicated more pronounced hypoalbuminemia in pediatric patients, with a pooled SMD of -1.08 g/dL (95% CI: -1.71 to -0.45). Our analysis demonstrated the link between hypoalbuminemia and severe dengue, indicating a significant pooled relative risk of 2.286, within 95% CI 1.308 to 3.996. CONCLUSIONS: The study confirms hypoalbuminemia as a significant predictor of severe dengue. Recognizing hypoalbuminemia in dengue patients can aid clinicians in forecasting the severity, potentially improving patient outcomes through targeted therapeutic strategies.
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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 | 0.012 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.029 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".