Bibliographic record
Abstract
INTRODUCTION: About 60% to 85% of people infected with hepatitis C virus will go on to develop chronic hepatitis C, which is now believed to affect 3% of the world's population. METHODS AND OUTCOMES: We conducted a systematic overview and aimed to answer the following clinical questions: What are the effects of interferon-free treatments in treatment-naïve people with chronic hepatitis C infection without cirrhosis? What are the effects of interferon-free treatments in treatment-naïve people with chronic hepatitis C infection with cirrhosis? We searched: Medline, Embase, The Cochrane Library, and other important databases up to August 2014 (Clinical Evidence overviews are updated periodically; please check our website for the most up-to-date version of this review). RESULTS: After deduplication and removal of conference abstracts, 30 records were screened for inclusion in the review. Appraisal of titles and abstracts led to the exclusion of 11 studies and the further review of 19 full publications. Of the 19 full articles evaluated, two systematic reviews and one RCT were added. We performed a GRADE evaluation for two PICO combinations. CONCLUSIONS: In this systematic overview, we categorised the efficacy for 12 different intervention/comparison combinations, based on information relating to the effectiveness and safety of sofosbuvir (with or without ribavirin), sofosbuvir (with or without ribavirin) plus ledipasvir, and sofosbuvir (with or without ribavirin) plus simeprevir, all in people with and without cirrhosis.
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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.002 |
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".