IDDF2024-ABS-0194 Real-world data with pangenotypic direct-acting antivirals: preliminary results of the SVR10K study
Bibliographic record
Abstract
Background A previous real-world data analysis demonstrated the high effectiveness of sofosbuvir/velpatasvir (SOF/VEL) in > 6,000 HCV patients from 12 clinical cohorts across Australia, Canada, Europe & USA. Expanding this research initiative to include even more patients from additional geographical areas will allow us to show SOF/VEL effectiveness across multiple diverse populations & the evaluation of HCV patient characteristics across Western countries, Asia, Middle Eastern, and Latin-American regions. Methods This real-world analysis includes patients ≥ 18 years treated with SOF/VEL without RBV for 12 weeks, as decided by the treating HCP, from 7 sites across Hong Kong, Mexico, Sweden, Spain, Taiwan, and the United Arab Emirates. Age, sex, treatment-experienced (TE), cirrhosis stage (no decompensated included), genotype, coinfections, time to treatment initiation (TTI) from HCV diagnosis, and SVR (4/12/24) were analyzed. Results Overall, 4,679 patients were included, 51% of them from Asian countries. Median age was 56.9 [IQR 46-66], where males were 59%, and age > 50 years in 68%. Genotype 3 was present in 25%, F4 21%, TE 5%, while HIV, HBV and HDV coinfection was reported in 4.7%, 4.3%, and 0.1%, respectively. The TTI was available in 74%, with 17% having ≤30 days. In terms of effectiveness, SVR was achieved in 98.4% of the treated population and 99% of Asian countries. Conclusions Results on treatment effectiveness in these new geographies did not differ from real-world studies of patients in Western countries, reinforcing that HCV treatment guidelines are globally applicable and supporting the efficacy of pDAA therapy.
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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 | 0.015 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".