Histological progression of chronic renal allograft injury comparing sirolimus and mycophenolate mofetil–based protocols. A single‐center, prospective, randomized, controlled study
Bibliographic record
Abstract
Blydt‐Hansen TD, Gibson IW, Birk PE. Histological progression of chronic renal allograft injury comparing sirolimus and mycophenolate mofetil–based protocols. A single‐center, prospective, randomized, controlled study. Pediatr Transplantation 2010: 14:909–918. © 2010 John Wiley & Sons A/S. Abstract: In an effort to mitigate progression of IF/TA associated with chronic renal allograft injury, we hypothesize that adjuvant immunosuppression with sirolimus (SRL) will delay progression compared with MMF. Subjects 5–17 yr old, >1‐yr post‐transplant with mild or moderate IF/TA (Banff criteria) and tacrolimus dose minimization were randomized to continue MMF or convert to SRL and followed for two yr. For the entire cohort (n = 20), there was significant progression of %GGS, ci, ct, cv, and ah scores over the follow‐up period (p < 0.05). There was no difference in rates of progression of Banff scores, %GGS, or % IF over two yr between the two groups, though power was low. Both groups exhibited similar rates of eGFR decline (MMF: −12.3 vs. SRL: −11.8 mL/min/1.73 m2/yr), which was correlated with ct score (p < 0.05). The SRL group had more episodes of acute allograft dysfunction and oral ulcers. Proteinuria at 24 months was significantly increased in the SRL group (6/9 subjects) but was not correlated with eGFR or %GGS. We conclude that neither MMF nor SRL, combined with low‐dose tacrolimus, was effective at mitigating progressive histological changes or functional decline associated with chronic renal allograft injury.
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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 | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".