Corrigendum to ‘Assessment of long-term outcomes post living liver donation highlights the importance of scientific integrity when presenting transplant registry data’ [American Journal of Transplantation. Volume 22, Issue 6, March 2023, Pages 1519-1522]
Bibliographic record
Abstract
The authors regret that the appendix, detailing the names of The North American Living Liver Donation Innovation Group (NALLDIG), included a spelling error for one name. “Indiana University (AKubal)” should read “Indiana University (CKubal)” The authors would like to apologise for any inconvenience caused. Assessment of long-term outcomes post living liver donation highlights the importance of scientific integrity when presenting transplant registry dataAmerican Journal of TransplantationVol. 22Issue 6PreviewLiving donor liver transplantation has expanded in recent years, particularly in North America. As experience with this procedure has matured over the last 25 years, centers are increasingly faced with potential living donors who are more medically complex. As donors move through the evaluation process, completing the informed consent process continues to be challenged by a paucity of granular data demonstrating long-term outcomes and overall safety specifically in the otherwise “healthy” living liver donor population. Full-Text PDF Open Access
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 | 0.007 | 0.067 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.013 | 0.019 |
| Insufficient payload (model declined to judge) | 0.033 | 0.037 |
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".