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 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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads agree on what is shown here.
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".