Prevalence of acute kidney injury among dengue cases: a systematic review and meta-analysis
Bibliographic record
Abstract
Numerous studies have shown a correlation between dengue virus (DENV) infection and kidney disease. However, there is no existing meta-analysis on the prevalence of kidney diseases in the dengue population. A thorough systematic review and meta-analysis were undertaken to determine the prevalence of renal problems in people with DENV infection in order to fill this knowledge gap. A rigorous electronic literature search was carried out up to 25 January 2023 in a number of databases, including ProQuest, EBSCOhost, Scopus, PubMed and Web of Science. The search aimed to find articles that reported on the prevalence of kidney diseases in patients with DENV infection. Using the modified Newcastle-Ottawa Scale, the quality of the included studies was assessed. The meta-analysis included a total of 37 studies with 21 764 participants reporting on the prevalence of acute kidney injury (AKI) in individuals with DENV infection. The pooled prevalence of AKI in dengue patients was found to be 8% (95% confidence interval 6 to 11), with high heterogeneity across studies. The studies included are of moderate quality. The study revealed a high AKI prevalence in dengue patients, underlining the need for regular renal examination to detect AKI early and reduce hospitalization risk. Further research is needed to understand the dengue-kidney relationship and develop effective management 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 | 0.011 | 0.026 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.033 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".