Corrigendum to ‘Transplant oncology – Current indications and strategies to advance the field’ [JHEP Reports 6 (2024) 100965]
Bibliographic record
Abstract
It has come to our attention that there was an error in Table 6 of our manuscript. In the column ‘Recurrence’, it should read ‘3-4 points: high risk’ not ‘2–4 points: high risk’. The table has been corrected online. Transplant oncology – Current indications and strategies to advance the fieldJHEP ReportsVol. 6Issue 2PreviewLiver transplantation (LT) was originally described by Starzl as a promising strategy to treat primary malignancies of the liver. Confronted with high recurrence rates, indications drifted towards non-oncologic liver diseases with LT finally evolving from a high-risk surgery to an almost routine surgical procedure. Continuously improving outcomes following LT and evolving oncological treatment strategies have driven renewed interest in transplant oncology. This is not only reflected by constant refinements to the criteria for LT in patients with HCC, but especially by efforts to expand indications to other primary and secondary liver malignancies. 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 | 0.003 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.259 | 0.201 |
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".