IDDF2020-ABS-0061 Impact of treatment with tenofovir alafenamide (TAF) or tenofovir disoproxil fumarate (TDF) on hepatocellular carcinoma (HCC) incidence in patients with chronic hepatitis B (CHB)
Bibliographic record
Abstract
Background Potent antivirals can reduce HCC incidence in CHB. TDF and TAF are first-line treatments, and in Phase 3 studies through 3 years, TAF has shown antiviral efficacy similar to TDF, higher rates of ALT normalization, and no resistance. We evaluated HCC incidence in patients participating in these ongoing studies. Methods HBeAg-positive (n=1039) and -negative (n=593) patients with HBV DNA 20,000 IU/mL and ALT >60 U/L (males) or >38 U/L (females) were randomized (2:1) to TAF 25 mg QD or TDF 300 mg QD for up to 3 years, followed by open-label TAF through Year 8. Patients with hepatic decompensation, co-infection with HCV/HDV/HIV, or evidence of HCC were excluded. HCC was assessed at 6 monthly intervals by hepatic ultrasonography beginning after Week 96 and by local standards of care. The standardized incidence ratio (SIR) for HCC was calculated for observed cases relative to predicted cases using the REACH-B model. Results 1632 patients were followed for up to 4 years; HCC was seen in 16 patients (0.98%; 7 TAF; 9 TDF); median time to onset was 568 days. At baseline HCC patients were older (median age 53 vs 40 y; p<0.001), had lower median HBV DNA (6.2 vs 7.3 log10 IU/mL; p=0.041) and were more likely to have cirrhosis (FibroTest score 0.75; 31% vs 10%; p=0.004). For study patients, the overall SIR was significantly reduced with TAF or TDF treatment 0.45 (95% CI 0.278 -0.740) (table 1). HCC incidence was significantly reduced (SIR 0.42, 95% CI 0.23 to 0.75) in noncirrhotic patients (n=11 vs 26.5 predicted), but not for cirrhotic patients (n=5 vs 8.1 predicted). The SIR was significantly reduced in noncirrhotic patients receiving TAF (n=5), but not in those with TDF (n=6). Conclusions In CHB patients treated with TAF or TDF for up to 4 years, HCC incidence was reduced, particularly in noncirrhotic patients. Additional follow up is needed to further characterize the impact of longer-term treatment on HCC risk reduction.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 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".