The impact of sirolimus on hepatocyte proliferation after living donor liver transplantation
Bibliographic record
Abstract
Toso C, Patel S, Asthana S, Kawahara T, Girgis S, Kneteman NN, Shapiro AMJ, Bigam DL. The impact of sirolimus on hepatocyte proliferation after living donor liver transplantation. Clin Transplant 2009 DOI: 10.1111/j.1399‐0012.2009.01159.x © 2009 John Wiley & Sons A/S. Abstract: Background: There is a lack of data on the use of sirolimus after partial liver transplantation, especially regarding its impact on post‐transplant regeneration. Methods: We reviewed adult living donor transplantations, with de novo sirolimus (n = 7) and without sirolimus (n = 21). Liver biopsies were stained for KI‐67, a proliferation marker. Controls included specimens with normal liver parenchyma (n = 13). Results: Both groups had similar demographics, graft and patient survival and complication rates. During the first six wk and over the whole first year post‐transplant, the use of sirolimus was associated with lower levels of hepatocyte proliferation compared to sirolimus‐free patients, (overall, 0.3 [0–7.2] vs. 3 [0–49] KI‐67 positive hepatocytes per high power field, p ≤ 0.05). The levels observed in the sirolimus group were similar to those seen in non‐transplanted control patients with normal parenchyma (0.2 [0–1.3], p = NS). Post‐transplant hepatocyte proliferation correlated with the serum levels of sirolimus (p ≤ 0.05), but not with those of tacrolimus or with the dose of mycophenolate mofetil (p = 0.9 and 0.3, respectively). Conclusions: These data suggest that sirolimus is associated with decreased post‐transplant hepatocyte proliferation. The clinical significance of this observation remains to be fully determined.
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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 | 0.001 | 0.001 |
| 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) | 0.001 | 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; 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".