A study on serological detection of Hepatitis A virus with associated risk factors in young kids
Bibliographic record
Abstract
Background: Hepatitis A virus (HAV) is a major concerning issue for human health that causes acute viral hepatitis. Hepatitis A virus is non enveloped RNA virus which is a member of the family Picornaviridae and genus Hepatovirus. HAV is more prevalent in developing countries like Pakistan with poor sanitation and economic status. The virus is present in young kids of age 1 to 15 years old associated with many risk factors.Methods: A total of 100 blood samples (1-2 ml) were collected from the hospitals of Lahore. After serum collection, indirect ELISA was performed on a commercially available kit. The Optical density (OD) was taken from the ELISA reader. Positive and negative samples were also run along with the samples and samples OD was compared with the OD of positive and negative controls. Cut off value was calculated by multiplying negative control (NC) with 2.1. The absorbance value of specimen/cutoff <1: samples having a value less than cut-off value were considered negative and samples having a value greater than or equal to cut-off value were considered positive. The apparent prevalence of Hepatitis A virus was measured by dividing the number of children positive to the total number of children included in the study.Results: Of the 100 samples tested, 37% were positive for IgM and 100% were positive for IgG. Statistical Analysis SPSS 21.0 version was applied to analyze the data and a correlation test was applied to see the association of risk factors with disease status.Conclusion: This study was done to appraise the overall status of HAV prevalence in young kids in association with potential risk factors.Keywords: Hepatitis A; ELISA; Risk assessment; Prevalence; Hygiene
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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 | 0.001 | 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 | 0.001 | 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".