Duration of Fever in Patients with Dengue: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Dengue is an acute febrile illness endemic to tropical countries and associated with high mortality rates. Despite being a viral infection, there is rampant misuse of antibiotics in patients with dengue because of perceived delay in defervescence and fear of secondary bacterial infections. Therefore, there is a need to establish the average fever duration with a confidence interval among patients with dengue. Studies up to October 21, 2022 from two databases (PubMed and Embase) were included using the search terms related to dengue and duration of fever. All retrieved articles were screened for eligibility by two independent reviewers. Studies where the average duration of fever was available were included for systematic review. Articles with at least more than 20 patients where a mean and standard deviation for the total duration of fever was available were included for meta-analysis. A total of 643 articles were included from the two databases after duplicate deletion. After two rounds of screening, 31 articles (n = 7,905) were finally included. The mean duration of fever in the 20 articles included for meta-analysis was 5.1 (95% CI: 4.7-5.5) days. Longer duration of fever was seen in those with a higher grade of fever, those with higher disease severity, and those with concurrent bacterial infections. In the absence of risk factors for concurrent bacteremia, antimicrobials may be unnecessary in those with dengue fever duration of less than 5.5 days.
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 | 0.010 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.037 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".