Safety and tolerability of the <scp>T</scp>‐cell depletion protocol coupled with anakinra and etanercept for clinical islet cell transplantation
Bibliographic record
Abstract
BACKGROUND: Islet cell transplantation (ICT) is a promising approach to cure patients with type 1 diabetes. We have implemented a new immunosuppression protocol with antithymoglobulin plus anti-inflammatory agents of anakinra and eternacept for induction and tacrolimus plus mycophenolate mofetil for maintenance [T-cell depletion with anti-inflammatory (TCD-AI) protocol], resulting in successful single-donor ICT. METHODS: Eight islet recipients with type 1 diabetes reported adverse events (AEs) monthly. AEs were compared between three groups: first infusion with the TCD-AI protocol (TCD-AI-1st) and first and second infusion with the Edmonton-type protocol (Edmonton-1st and Edmonton-2nd). RESULTS: The incidence of symptomatic AEs within the initial three months in the TCD-AI-1st group was less than in the Edmonton-1st and Edmonton-2nd groups, with a marginally significant difference (mean ± SE: 5.5 ± 0.3, 7.5 ± 0.5, and 8.3 ± 1.3, respectively; p = 0.07). A significant reduction in liver enzyme elevation after ICT was found in the TCD-AI-1st group compared with the Edmonton-1st and Edmonton-2nd groups (p < 0.05). Because of AEs, all patients in the Edmonton protocol eventually converted to the TCD-AI protocol, whereas all patients tolerated the TCD-AI protocol. CONCLUSIONS: TCD-AI protocol can be tolerated for successful ICT, although this study includes small cohort, and large population trial should be taken.
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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 | 0.002 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".