Single Topic Conference on Autoimmune Liver Disease from the Canadian Association for the Study of the Liver
Bibliographic record
Abstract
Autoimmune liver disease (AILD) spans a spectrum of chronic disorders affecting the liver parenchyma and biliary system. Three main categories of AILD are autoimmune hepatitis (AIH), primary biliary cirrhosis (PBC), and primary sclerosing cholangitis (PSC). This review condenses the presentation and discussions of the Single Topic Conference (STC) on AILD that was held in Ottawa, Ontario, in November 2019. We cover generalities regarding disease presentation and clinical diagnosis; mechanistic themes; treatment paradigms; clinical trials, including approaches and challenges to new therapies; and looking beyond traditional disease boundaries. Although these diseases are considered autoimmune, the etiology and role of environmental triggers are poorly understood. AILDs are progressive and chronic conditions that affect survival and quality of life. Advances have been made in PBC treatment because second-line treatments are now available (obeticholic acid, bezafibrate); however, a significant proportion still present suboptimal response. AIH treatment has remained unchanged for several decades, and data suggest that fewer than 50% of patients achieve a complete response and as many as 80% develop treatment-related side effects. B-cell depletion therapy to treat AIH is in an early stage of development and has shown promising results. An effective treatment for PSC is urgently needed. Liver transplant remains the best option for patients who develop decompensated cirrhosis or hepatocellular carcinoma within specific criteria, but recurrent AILD might occur. Continued efforts are warranted to develop networks for AILD aimed at assessing geo-epidemiological, clinical, and biochemical differences to capture the new treatment era in Canada.
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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.009 | 0.006 |
| Insufficient payload (model declined to judge) | 0.209 | 0.062 |
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".