Experience with a Novel Efalizumab‐Based Immunosuppressive Regimen to Facilitate Single Donor Islet Cell Transplantation
Bibliographic record
Abstract
Islet transplantation is an experimental therapy for selected patients with type 1 diabetes (T1DM). It remains limited by immunosuppressive drug toxicity, progressive loss of insulin independence, allosensitization and the need for multiple islet donors. We describe our experience with an efalizumab-based immunosuppressive regimen as compared to the prevailing standard regimen, the Edmonton protocol. Twelve patients with T1DM received islet transplants: eight were treated with the Edmonton protocol; four were treated with daclizumab induction, a 6-month course of tacrolimus, and maintenance with efalizumab and mycophenolate mofetil. The primary endpoint was insulin independence after one islet infusion. Only two Edmonton protocol treated patients achieved the primary endpoint; six required islets from multiple donors, and all experienced leukopenia, mouth ulcers, anemia, diarrhea and hypertransaminasemia. Four became allosensitized. All patients treated with the efalizumab-based regimen achieved insulin independence with normal hemoglobin A1c after a single islet cell infusion and remained insulin independent while on efalizumab. These patients experienced significantly fewer side effects and none became allosensitized. Trial continuation was terminated by withdrawal of efalizumab from the market. These data suggest that this efalizumab-based regimen prevents islet rejection, is well tolerated, and allows for single donor islet transplantation.
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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 | 0.001 |
| 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".