Seroepidemiology of Dengue Viral Infection in Peshawar
Bibliographic record
Abstract
Background: Dengue viral infection is the most prevalent infection particularly in the months of September to December in different region of Khyber Pakhtunkhwa. The aim of this study was to determine the prevalence of dengue viral infection in district Peshawar. Methods: A cross-sectional study was conducted and recruited two hundred suspected dengue viral infected patients. Blood were collected for diagnosis of dengue viral infection through immunochromatograpy technique. All the collected data analyzed through Microsoft Excel 2020. Results: A total of 200 suspected dengue viral infected patients participated. Among total, 59.5% were male and 40.5% were female patients. Out of total, 61 were found positive through NS-1 strips. IgG antibodies were more found in male than female. Whereas, IgM antibodies were more prevalent found in female patients. Conclusion: Overall, the prevalence of dengue viral infection is more in our region. The prevalence of dengue viral infection is greater in male patients as compared to female patients. It is important to arrange different prevention programs including seminars, workshops and conference throughout the district. Implementation of control and surveillance programs are highly essential to determine regarding the dengue level. Health policy makers need to pay attention towards the dengue disease and to provide different training session to health care providers and physician. Keywords: Dengue viral infection, Sero-prevalence, Epidemiology
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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 | 0.000 |
| 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) | 0.002 | 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".