Immune monitoring of pancreatic islet graft: towards a better understanding, detection and treatment of harmful events
Bibliographic record
Abstract
IMPORTANCE OF THE FIELD: Long-term clinical outcomes of islet transplantation are hampered by rejection and recurrence of autoimmunity, which lead to a gradual decrease in islet function usually taking place over the first five years after transplantation. An accurate monitoring strategy could allow for the detection and treatment of harmful immune events, potentially resulting in higher rates of insulin-independence. AREAS COVERED IN THIS REVIEW: This article provides a critical review of the various assays currently available for the assessment of allo- and autoimmunity both prior to and after islet transplantation. The accuracy in predicting clinical outcome is specifically addressed. WHAT THE READER WILL GAIN: Most current tests based on the assessment of allo- and auto-immune antibody are of minimal help in clinical practice. Cell-based tests (including the assessment of cytotoxic T lymphocyte precursors, proliferation tests, enzyme-linked immunospot) have the potential to allow earlier and more accurate detection of harmful events. TAKE HOME MESSAGE: A specific and accurate immune monitoring has the potential to significantly improve islet transplant outcomes. The development and use of such tests (favouring cell-based tests) should be promoted.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 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 | 0.001 | 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".