Doxycycline vs azithromycin in patients with scrub typhus: a systematic review of literature and meta-analysis
Bibliographic record
Abstract
INTRODUCTION: Scrub typhus is a bacterial mite-borne disease associated with poor clinical outcomes if not treated adequately. The study aimed to compare the time to defervescence, clinical failure, mortality and treatment-related adverse effects of two common drugs (doxycycline and azithromycin) used for its treatment. METHODOLOGY: This was a systematic review and meta-analysis. All studies up to 20.03.2023 were screened for eligibility in Pubmed and Embase using a search string containing terms related to scrub typhus, doxycycline and azithromycin. After two phases of screening, all comparative studies where doxycycline and azithromycin were used to treat scrub typhus were included. The studies were critically appraised using standardised tools, and a meta-analysis was performed for time to defervescence (primary outcome), clinical failure, mortality and treatment-related adverse effects. RESULTS: Of 744 articles from two databases, ten were included in the meta-analysis. All but two studies had a high risk of bias. The meta-analysis for time to defervescence had a high heterogeneity and did not show any significant difference between doxycycline and azithromycin arms [Mean difference of -3.37 hours (95%CI: -10.31 to 3.57), p=0.34]. When the analysis was restricted to studies that included only severe scrub typhus, doxycycline was found to have a shorter time to defervescence [mean difference of -10.15 (95%CI: -19.83 to -0.46) hours, p=0.04]. Additionally, there was no difference between the two arms concerning clinical failure, mortality and treatment-related adverse effects. CONCLUSION: The current data from studies with a high risk of bias did not find statistically significant differences in clinical outcomes between doxycycline and azithromycin for scrub typhus.
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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 | 0.016 | 0.030 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.025 | 0.050 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 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".