Occult hepatitis <scp>B</scp> infection in a hemodialysis population in <scp>G</scp>uilan province, northern <scp>I</scp>ran
Bibliographic record
Abstract
Hemodialysis (HD) patients are vulnerable to transfusion-transmitted infections such as hepatitis B virus (HBV). HBV infection with undetectable hepatitis B surface antigens (HBsAg) is described as occult HBV and can lead to serious complications. The aim of this study was to evaluate the prevalence of occult HBV and concomitant factors in HD patients. Using a cross-sectional design, clinical and epidemiological data were obtained from May to September 2009 in 11 different HD units in Guilan province in northern Iran. After serological testing for HBV surface antigens in 514 HD patients using a third-generation enzyme-linked immunosorbent assay kit (Diapro, Milano, Italy), HBsAg-negative patients were tested for HBV DNA using a Qiagen PCR kit (Artus Qiagen GmbH, Hilden, Germany). After omission of seven HBsAg-positive patients, 507 patients were included in the study, 280 (55.2%) of whom were male and 227 (44.8%) were female. Patients ranged in age from 16 to 66 years (mean 53.2 years). No HBV DNA was detected in HBsAg-negative patients. Some 59 patients (11.6%) were anti-hepatitis C virus positive and 32 (6.3%) were hepatitis C virus positive according to polymerase chain reaction. The study results indicate that occult HBV infection is not a significant health problem in HD patients in Guilan province.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".