A snapshot of challenges and opportunities faced by the scientific workforce in liver transplantation—a survey of the International Liver Transplantation Society (ILTS)
Bibliographic record
Abstract
Basic and translational research (B&TR) in liver transplantation (LT) underwent considerable changes and shifts over the past decade. To capture the current landscape and future potential of B&TR in LT, we conducted an online survey within the International Liver Transplantation Society (ILTS) community. The survey aimed to collect comprehensive data on the respondents' characteristics, qualifications, experiences, and research activities, providing the present state and future directions of B&TR in LT. Between October 2023 and January 2024, an online survey consisting of 35 key items was distributed to the ILTS community through newsletters and social media channels. Data were analyzed using a combination of quantitative and qualitative methods. The survey gathered 153 valid responses, with 79% of respondents possessing relevant experience in B&TR and 76% reporting concurrent clinical duties. Some 62% hold faculty positions, with 34% identifying as MDs and 44% holding combined MD/PhD degrees. About 71% of scientists with clinical duties reported challenges in conducting B&TR, with 57% citing a lack of time and 41% pointing to insufficient funding. Nevertheless, 69% of respondents currently receive research funding, with 58% supported by government or public sources. Among early career researchers, 57% reported receiving average or poor mentoring, and 30% indicated insufficient protected time for research. Looking ahead, advancing technologies, machine learning/artificial intelligence, multi-omics, xenotransplantation, and machine perfusion were highlighted as areas with the potential to significantly shift the paradigm in the near future. Our survey captured insights from B&TR scientists within the ILTS, identifying both challenges and opportunities for future developments and aiding in the strategic direction of the society's initiatives.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".