Passive Facility-based fever surveillance for Dengue at the time of Chikungunya introduction in Colombia
Bibliographic record
Abstract
Background: The global burden of arboviruses has increased lately and while dengue has been endemic in Colombia, chikungunya was recently introduced in 2014. Methods & Materials: A passive facility-based fever surveillance study was conducted to estimate the age-specific incidence of symptomatic dengue cases and characterize its clinical profile in Colombia. Individuals with undifferentiated fever (<7 days), between 1-55 years, were followed-up for 28 days. Rapid diagnostic tests, serological and molecular analyses were done in paired samples to identify dengue and circulating serotypes. Underreport was assessed and age-specific multiplication factors were calculated. Results: From 839 febrile participants, 686 completed the study. There were 295 (33.2%) confirmed dengue infections (57.3% primary infections), and 191 (22.8%) chikungunya cases. Dengue cases were younger (median = 18 years; IQR = 12-29) than chikungunya cases (median = 25 years; IQR = 16-38). Presence of thrombocytopenia (OR:2.2; 95%CI = 1.3-3.8) and abdominal pain (OR:1.9; 95%CI = 1.3-3.0) were predictors of dengue, and the presence of rash (OR: 9.9; 95%CI = 5.3- 18.6) was the main predictor of chikungunya. All four DENV serotypes were circulating in the study area with DENV1 as the most prevalent 47%. Conclusion: This study highlights that despite the important level of underreporting, dengue is still an important cause of fever among all age groups in this setting. Moreover, dengue and chikungunya are present in Colombia and this study contributes to the evidence of their simultaneous ongoing transmission.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".