Translational Mini-Review Series on Type 1 Diabetes: Immune-based therapeutic approaches for type 1 diabetes
Bibliographic record
Abstract
Type 1 diabetes (T1D) is often considered the prototype organ-specific autoimmune disease in clinical immunology circles. The key disease features - precise destruction of a single endocrine cell type occurring on a distinct genetic and autoimmune background - have been unravelled in recent years to such an extent that there is a growing expectation that the disease should be curable. T1D is something of an orphan disease, currently managed by endocrinologists yet dependent upon the wit of immunologists, both basic and clinical, to find the best approaches to prevention and cure. Type 1 diabetes thus represents one of the most active arenas for translational research, as novel immune-based interventions find their way to the clinic. The first serious attempt at immune-based treatment for T1D was in 1984, the first at prevention in 1993; current and planned trials will take us into the next decade before reporting their results. This paper represents the first attempt at a comprehensive review of this quarter century of endeavour, documenting all the strategies that have emerged into clinical studies. Importantly, the intense clinical activity has established robust infrastructures for future T1D trials and frameworks for their design. The evident success of the monoclonal anti-CD3 antibody trials in established T1D demonstrate that modulation of islet autoimmunity in humans after the onset of overt disease can be achieved, and give some reason to be cautiously optimistic for the ability of these and other agents, alone and in combination, to provide an effective immunotherapy for the disease.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.037 | 0.017 |
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".