Bibliographic record
Abstract
Development of serological and nucleic acid testing (NAT) has revolutionized hepatitis C virus (HCV) diagnosis. Although third generation anti-HCV enzyme immunoassays (EIAs) are very effective for testing high prevalence populations, confirmatory testing is still necessary when these tests are applied to populations with a low HCV prevalence to exclude false positive results. Limitations of third generation anti-HCV EIAs include: the relatively prolonged time between acute infection and detection of seroconversion (which typically requires at least 5-6 weeks); delayed seroconversions in immunocompromised hosts (requiring months to years); and the inability of serological tests to confirm active HCV infection. In contrast, nucleic acid testing (NAT) can directly detect HCV RNA in serum, plasma or tissue and thereby confirm active infection as well as narrow the window between infection and HCV detection to as little as 1-2 weeks. Commercial NAT assays are now highly sensitive, specific, and reproducible and have largely replaced unreliable home brew nucleic acid amplification assays. Qualitative commercial NAT are typically more sensitive than quantitative assays and therefore the method of choice to confirm active infection. Given the efficacy of combination therapy with interferon/ribavirin and newer antiviral agents under development, HCV infection may become curable, which will likely impact future disease transmission. As the therapeutic costs are currently very high, there is clearly a need to assess the utility of quantitative NAT and to further evaluate the role of HCV genotyping to optimize antiviral therapy. Thus for the foreseeable future, a combination of both serological tests and NAT will be required for cost-effective HCV diagnosis and monitoring.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.023 | 0.021 |
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".