The management of liver disease in people with congenital bleeding disorders: guidance from European Association for Haemophilia and Allied Disorders, European Haemophilia Consortium, ISTH, and World Federation of Hemophilia
Bibliographic record
Abstract
People with bleeding disorders (PWBD) have been exposed to the risk of developing chronic viral hepatitis and cirrhosis after replacement therapy. Today, the advent of new pharmacologic strategies for the control of hemostasis and the efficacious antiviral therapies against hepatitis C virus and hepatitis B virus have significantly reduced this risk. However, the definitive success for liver health in this clinical setting is also influenced by other factors, such as the severity of liver disease at the time of hepatitis B virus/hepatitis C virus antiviral therapy and the exposure to highly prevalent factors of chronic liver damage (eg, metabolic dysfunction and/or alcohol) that can cause a residual risk of complications such as hepatocellular carcinoma, portal hypertension, and liver insufficiency. With this background, a group of experts selected among hepatologists, hematologists, PWBD treaters, and patient representatives produced this practical multisociety guidance for the protection of liver health and the prevention and management of liver complications in PWBD based on the most updated protocols of care.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".