Chikungunya Virus Infection and Diabetes Mellitus: A Double Negative Impact
Bibliographic record
Abstract
The impact of chikungunya virus (CHIKV) infection on diabetic patients (DPs) has not been described. We aimed to compare clinical features of CHIKV infection in DPs and nondiabetic patients (NDPs), and to evaluate its effects on glycemic control among DPs. We recorded clinical information and, in DPs, glycemic control. Forty-six DPs and 53 NDPs aged ≥ 20 years living in Haiti, with acute CHIKV infection, were studied. Diabetes duration was 7.1 ± 6.1 years. The most common acute CHIKV clinical manifestations were arthralgia (100.0% DPs and 98.1% NDPs, P = 1.000) and fever (86.9% DPs and 90.5% NDPs, P = 0.750). In DPs as compared with NDPs, arthralgia was more intense (mean pain score of 6.0/10 ± 2.2 versus 5.1/10 ± 2.0, P = 0.04) and took longer to improve (8.2 ± 3.0 versus 3.5 ± 2.5 days, P < 0.0001). Severe arthralgia was more prevalent (58.7% versus 20.8%, P = 0.0002), as was myalgia (80.4% versus 50.9%, P = 0.003), and fever lasted longer (5.1 ± 1.8 versus 3.7 ± 1.7 days, P = 0.0002). Among DPs, median fasting capillary glucose before versus after disease onset was 132.5 and 167.5 mg/dL (P < 0.001), corresponding to a median increase of 26.8% (interquartile range: 14.4-50.1%). Antidiabetic medication was titrated up in 41.3%. In summary, among DPs, CHIKV infection has a significant negative impact on glycemic control and, compared with NDPs, results in greater morbidity. Close clinical and glycemic observation is recommended in DPs with CHIKV infection.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".