High false‐negative rate of anti‐<scp>HCV</scp> among <scp>E</scp>gyptian patients on regular hemodialysis
Bibliographic record
Abstract
Routine serological testing for hepatitis C virus (HCV) infection among hemodialysis (HD) patients is currently recommended. A dilemma existed on the value of serology because some investigators reported a high rate of false-negative serologic testing. In this study, we aimed to detect the false-negative rate of anti-HCV among Egyptian HD patients. Seventy-eight HD patients, negative for anti-HCV, anti-HIV, and hepatitis B surface antigen, were tested for HCV RNA by reverse transcriptase polymerase chain reaction (RT-PCR). In the next step, the viral load was quantified by real-time PCR in RT-PCR-positive patients. Risk factors for HCV infection, as well as clinical and biochemical indicators of liver disease, were compared between false-negative and true-negative anti-HCV HD patients. The frequency of false-negative anti-HCV was 17.9%. Frequency of blood transfusion, duration of HD, dialysis at multiple centers, and diabetes mellitus were not identified as risk factors for HCV infection. The frequency of false-negative results had a linear relation to the prevalence of HCV infection in the HD units. Timely identification of HCV within dialysis units is needed in order to lower the risk of HCV spread within the HD units. The high false-negative rate of anti-HCV among HD patients in our study justifies testing of a large scale of patients for precious assessment of effectiveness of nucleic acid amplification technology testing in screening HD patient.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".