IDDF2024-ABS-0205 Impact of long-term treatment with continuous tenofovir alafenamide (TAF) or after switch from tenofovir disoproxil fumarate (TDF) on hepatocellular carcinoma (HCC) incidence in patients with chronic hepatitis B (CHB)
Bibliographic record
Abstract
Background In an integrated analysis of 2 global Phase 3 studies, we evaluated HCC incidence and risk at 8 years in patients treated with TAF and those treated initially with TDF and then switched to TAF for up to 6 years (y). Methods In 2 studies, HBeAg-positive (n=859) and -negative (n=439) patients with HBV DNA ≥20,000 IU/mL and ALT >60 U/L (males) or >38 U/L (females) were randomized to TAF or TDF in a double-blind (DB) phase for up to 3 y, followed by open-label (OL) TAF through Y8. HCC was assessed by local standards of care and by hepatic ultrasonography. Three validated models (REACH-B, aMAP, and mPAGE-B) were utilized to assess HCC risk by initial treatment assignment and collectively. Using the REACH-B model, standard incidence ratios (SIRs) for HCC were calculated. Results Through Y8, HCC was diagnosed in 21/1298 patients (1.6%; TAF 1.4%; TDF→TAF 2.1%; P=0.33)— 11 in DB/10 in OL phase. Eight of 21 HCC cases were in cirrhotic patients. The median time to HCC onset was 729 days (TAF 1291, TDF→TAF 460 days). Advanced age, male, and cirrhosis were more common in HCC vs non-HCC patients (P<0.05). Proportionately, more HCC patients were HBV genotype C (76% vs 47%) and had BL HBV DNA between 6 to ≤ 8 log10IU/mL (57% vs 38%). With treatment over 8y, by REACH-B, HCC incidence was significantly reduced (21 observed vs 74.6 predicted; SIR [95% CI] 0.28; P<0.0001; figure 1). Of patients predicted to be low risk for HCC at BL, nearly all remained low risk at Y8 by aMAP (98%) and mPAGE-B (97%), and substantial proportions estimated to be medium or high risk at BL shifted to lower risk at Y8 (aMAP: 45% and 72%; mPAGE-B: 27% and 51%, respectively). Conclusions CHB patients treated with TAF alone or switched from TDF to TAF for up to 8 y showed a reduced observed vs predicted risk for HCC development.
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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 | 0.002 | 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".