Risk factors for mortality in patients with chikungunya: A systematic review and meta‐analysis
Bibliographic record
Abstract
INTRODUCTION: Chikungunya fever is a debilitating arthritic disease that can lead to atypical severe complications and sometimes be fatal. The risk factors for fatal outcomes of chikungunya fever have not been thoroughly studied. This systematic review and meta-analysis aimed to identify mortality risk factors in patients with chikungunya. These findings will aid clinicians in targeting high-risk groups with severe chikungunya for timely interventions, ultimately improving patient outcomes. OBJECTIVE: The objective of this study is to identify mortality risk factors in patients with chikungunya. METHODS: We conducted a systematic review and meta-analysis by searching the MEDLINE, Embase, Cochrane, BVS, BDTD and OpenGrey databases to identify eligible observational studies on patients with chikungunya. These studies analysed mortality risk factors, providing adjusted risk measures along with their corresponding confidence intervals (CIs). We estimated the pooled weighted mean difference and 95% CIs using a random-effects model, and the methodological quality was assessed using the Newcastle-Ottawa Scale. RESULTS: Our search yielded a total of 334 records. After removing duplicates, we screened 275 records, reviewed 31 full articles and included seven studies in the systematic review and four in the meta-analysis, with a total of 220,215 patients and 908 fatal cases. Diabetes Mellitus (OR = 2.86, 95% CI 1.75-4.69), hypertension (OR = 3.10, 95% CI 2.02-4.77), age ≥ 60 years (OR = 19.49, 95% CI 1.98-191.88), chronic kidney disease (OR = 5.81, 95% CI 1.30-25.99), male sex (OR = 2.07, 95% CI 1.71-2.51) and vomiting (OR = 2.18, 95% CI 1.75-2.73) are significantly and positively associated with mortality in chikungunya. CONCLUSION: Elderly men with chronic diseases have a higher risk of death from chikungunya; therefore, they deserve more careful evaluation.
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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 | 0.013 | 0.031 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.040 |
| Bibliometrics | 0.008 | 0.009 |
| 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".