Hepatitis C seroprevalence and correlation between viral load and viral genotype among primary care clients in Mexico.
Bibliographic record
Abstract
OBJECTIVE: To measure hepatitis C virus (HCV) sero-prevalence, prevalence, hepatitis risk characteristics frequency, and genotype correlation with viral load among clients attending health care clinics. MATERIAL AND METHODS: Venous blood samples from l12 226 consecutive consenting adults were collected from January 2006 through December 2009. HCV antibodies were detected by immunoassay. HCV RNA was detected by qRT-PCR and viral genotype was performed by PCR and LIPA test. RESULTS: The HCV seroprevalence observed was l.5 % (C.I. 95% l.3-l.7), from seropositive individuals 60.9 % reported previous blood transfusion, 28.3% declared to have relatives with cirrhosis, 25.2% had tattoos or piercings, and 6.9% referred to have used drugs. Male gender and transfusion (p<0.001) were the most frequent hepatitis risk characteristics in the HCV seropositive group. Among seropositive subjects 48.3% presented HCV RNA.The most frequent genotype detected in all geographic areas of Mexico was l (subtype lA, 33%; subtype lB, 21.4%) followed by genotype 2 (subtype 2A, 8.50%). Subjects with genotype 1 had a significant correlation with the highest viral load. CONCLUSIONS: Our results show that nearly half of seropositive individuals are chronically infected. HCV infection has been shown in this study to be an emerging health problem in Mexico.
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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 | 0.000 | 0.000 |
| 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) | 0.003 | 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".