Hepatitis B Virus ( <scp>HBV</scp> ) Treatment Eligibility in the <scp>UK</scp> : Retrospective Longitudinal Cohort Data to Explore the Impact of Changes in Clinical Guidelines
Bibliographic record
Abstract
Nucleos/tide analogue (NA) drugs are used for long-term treatment of chronic hepatitis B virus (HBV) infection, with treatment eligibility criteria changing rapidly amidst globally evolving clinical guidelines. We aimed to quantify the prescription of NA drugs to date, and to undertake a preliminary assessment of the impact of relaxing treatment eligibility thresholds, leveraging a unique large real-world secondary care dataset. We assimilated longitudinal clinical data, collected between February 1997 and April 2023 from adults with chronic HBV infection from six centres in England through the UK NIHR Health Informatics Collaborative (HIC) Viral Hepatitis and Liver Disease theme. We describe factors currently associated with the receipt of NA treatment and determine the proportion of the population who would become treatment eligible as thresholds change. Across 7558 adults with a mean follow-up of 4.0 years (SD 3.9), NA treatment was prescribed in 2014/7558 (26.6%), and as expected according to guidelines at the time, was associated with HBV e-antigen (HBeAg) positivity and alanine transferase (ALT) above the upper limit of normal (> ULN). Treatment was more likely in males, older adults, in Asian and Other ethnicities (compared to White), and less likely in socioeconomically deprived individuals. The proportion of treatment-eligible individuals was 32.3% based on 2 records of ALT > ULN over 6-12 months, 41.7% based on ALT > ULN and viral load (VL) > 2000 IU/mL, and 95.1% based on detectable VL and either ALT > ULN or age > 30 years. Evolving clinical guidelines will lead to substantial increases in the proportion of individuals living with HBV who are eligible for treatment, underlining the need for services to adapt rapidly to the changing clinical environment.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".