Improved outcome with immunosuppressive monotherapy after renal transplantation in Schimke‐immuno‐osseous dysplasia
Bibliographic record
Abstract
SIOD is a multisystem disorder caused by a mutant chromatin remodelling protein. The main clinical findings are spondyloepiphyseal dysplasia with disproportionate growth restriction, defective cellular immunity, and steroid-resistant nephrotic syndrome secondary to biopsy proven FSGS leading to ESRF. Concerning ESRF, kidney transplantation is the therapy of choice since FSGS does not recur in the graft. However, with respect to the underlying immune disorder and the increased susceptibility to life threatening infections, the question of the optimal immunosuppressive therapy after renal transplantation remains unresolved. Under conventional immunosuppressive regimens some SIOD patients have developed severe disseminated cutaneous papilloma virus infections or EBV associated lymphoproliferative disease. We present several cases of children with SIOD (four of five had SMARCAL1 mutations) and monotherapy maintenance immunosuppression after renal transplantation and compare them with 13 patients from the SIOD registry. We have found that post-renal transplantation immunosuppressive monotherapy results in a good outcome with a reduced number of severe infections. Due to the underlying immunodeficiency in SIOD, limited immunosuppression may be possible without increasing the risk of acute or chronic rejection.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".