Erratum to: Factors predicting survival after post‐transplant hepatocellular carcinoma recurrence
Bibliographic record
Abstract
The following erratum and correction were submitted by the authors and were approved for publication by the editor: We have recently worked further on our database of patients with post-transplant hepatocellular carcinoma (HCC) recurrence and have realized that a systematic error was made in the analysis conducted for our original publication. The error was linked to the calculation of post-recurrence survival, which was shorter than originally stated. Instead of the published median survival of 18.8 ± 6.8 months, the true survival was of 6.3 ± 1.2 months (with a mean of 10.6 ± 3.0 months). As a result, the appropriate Fig. b is as follows: In addition, the results of the multivariate analysis looking for factors predicting the chance of post-recurrence survival have changed. The time between transplant and recurrence is no longer significantly predicting post-recurrence survival. This observation contrasts with those reported in previous publications (Taketomi et al., Ann Surg Oncol 2010; Kornberg et al., Eur J Surg Oncol 2010; Shin et al., Liver Transpl 2010). The occurrence of a rejection during the first 6 months after transplantation remained with the trend as a predictor of post-recurrence survival [HR 7.84 (0.85–72.50), p = 0.07]. The inclusion within Milan criteria at the time of transplantation [HR 4.11 (1.30–13.00), p = 0.016] was a significant predictor.
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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 | 0.004 | 0.054 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.013 | 0.009 |
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".