Banff 2022 Liver Group Meeting report: Monitoring long-term allograft health
Bibliographic record
Abstract
The Banff Working Group on Liver Allograft Pathology met in September 2022. Participants included hepatologists, surgeons, pathologists, immunologists, and histocompatibility specialists. Presentations and discussions focused on the evaluation of long-term allograft health, including noninvasive and tissue monitoring, immunosuppression optimization, and long-term structural changes. Potential revision of the rejection classification scheme to better accommodate and communicate late T cell-mediated rejection patterns and related structural changes, such as nodular regenerative hyperplasia, were discussed. Improved stratification of long-term maintenance immunosuppression to match the heterogeneity of patient settings will be central to improving long-term patient survival. Such personalized therapeutics are in turn contingent on a better understanding and monitoring of allograft status within a rational decision-making approach, likely to be facilitated in implementation with emerging decision-support tools. Proposed revisions to rejection classification emerging from the meeting include the incorporation of interface hepatitis and fibrosis staging. These will be opened to online testing, modified accordingly, and subject to consensus discussion leading up to the next Banff conference.
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 | 0.011 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.012 | 0.006 |
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".