What Is Hot and New in Basic and Translational Science in Liver Transplantation in 2023? Report of the Basic and Translational Research Committee of the International Liver Transplantation Society
Bibliographic record
Abstract
The 2023 Joint Annual Congress of the International Liver Transplantation Society, European Liver and Intestine Transplant Association, and Liver Intensive Care Group of Europe were held in Rotterdam, the Netherlands, from May 3 to 6, 2023. This year, all speakers were invited to attend the Congress in person for the first time since the COVID-19 pandemic. The congress was attended by 1159 registered delegates from 54 countries representing 5 continents, with the 10 countries comprising the bulk of the delegates. Of the 647 abstracts initially submitted, 542 were eventually presented at the meeting, coming from 38 countries (mainly North America, Europe, and Asia) and 85% of them (462 abstracts) came from only 10 countries. Fifty-three (9.8%) abstracts, originated from 17 countries, were submitted under the Basic/Translational Scientific Research category, a similar percentage as in 2022. Abstracts presented at the meeting were classified as (1) ischemia and reperfusion injury, (2) machine perfusion, (3) bioengineering and liver regeneration, (4) transplant oncology, (5) novel biomarkers in liver transplantation, (6) liver immunology (rejection and tolerance), and (7) artificial intelligence and machine learning. Finally, we evaluated the number of abstracts commented in the Basic and Translational Research Committee-International Liver Transplantation Society annual reports over the past 5 y that resulted in publications in peer-reviewed journals to measure their scientific impact in the field of liver transplantation.
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 | 0.172 | 0.092 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.013 | 0.010 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.025 | 0.010 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.013 | 0.010 |
| Insufficient payload (model declined to judge) | 0.010 | 0.007 |
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".