Epidemiological Survey of Hepatitis C Virus Infection in Fife, Scotland
Bibliographic record
Abstract
BACKGROUND: HCV infection is of growing public health importance in Scotland. We aim to establish: patient demographics; risk category; year/country of probable infection; referral/follow-up status; and genotypic variance of HCV in Fife. METHODS: Details of all HCV antibody positive patients, referred and assessed at specialist clinics in NHS Fife, until 1st of May 2007 were obtained retrospectively from the Fife hepatitis C database. RESULTS: In these patients, the ratio of males: female was 2:1, mean age was 36 years, representing a relatively young population, 27.4% of the patients consumed alcohol and 52.4% were smokers. Twelve patients were HIV/HCV co-infected (3.3%). Among the patients, 6.8% had serological evidence of past HBV exposure, 0.5% of patients were HCV/HBV co-infected and 18.8% were vaccinated. Eighty-six percent acquired HCV through injecting drug use and most cases were relatively newly acquired. Referral numbers were on the increase. Thirty-three of patients were under follow-up. Sixty-five percent of patients were genotype 2/3 and 35% were Genotype 1. CONCLUSIONS: Clear patterns were observed in terms of age group, gender, geographical distribution and risk category to facilitate the effective targeting of resources. HCV population in Fife are relatively young, have acquired HCV recently and are mostly of genotype 2/3. This may have a favourable influence on disease progression and cost implications of treating HCV in Fife.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".