Hepatitis C asociada al abuso de sustancias: nunca tan cerca de un tratamiento sin Interferón
Bibliographic record
Abstract
With 3-4 million of new infections occurring annually, hepatitis C virus (HCV) infection is a global Public Health problem. In fact, hepatitis C virus infection is one of the leading causes of liver disease in the world; in Western countries, two thirds of the new HCV infections are associated with injection drug use. The treatment of hepatitis C will change in the coming years with the irruption of new anti-HCV drugs, the so called Direct Antiviral Agents (DAA) that attack key proteins of the HCV life cycle. The new antiviral drugs are effective, safer and better tolerated. The 2014 WHO HCV treatment guidelines include some of them. The new DAA are used in combination and it is expected that Interferon will be not necessary in future treatment regimens against HCV infection. The irruption of new and potent antivirals mandate the review of the current standards of care in the HCV infected population. More inclusive and proactive treatment policies will be necessary in those individuals with substance use disorders.
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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".