IA-2 Antibody-Negative Status Predicts Remission and Recovery of C-Peptide Levels in Type 1 Diabetic Patients Treated With Cyclosporin
Bibliographic record
Abstract
OBJECTIVE: The use of cyclosporin in recent-onset type 1 diabetes has demonstrated the potential for immune intervention in the treatment and prevention of the disease. However, a proportion of patients failed to respond to cyclosporin treatment. Indicators of resistance to immune intervention would be valuable for the most effective use of such therapies in disease prevention. The aim of this study was to determine whether presence of IA-2 antibodies is such a marker. RESEARCH DESIGN AND METHODS: IA-2 antibodies were determined by radioligand binding assay in sera from patients recruited into the Canadian-European cyclosporin trial. Insulin dose requirements and glucagon-stimulated C-peptide secretion were analyzed in patients grouped according to IA-2 antibody status at entry. RESULTS: Cyclosporin treatment had no significant effect on frequency of IA-2 antibodies during the 1 year of treatment. Cyclosporin caused significant reduction in insulin requirements and significant increases in C-peptide secretion mainly in patients negative for IA-2 antibodies. Analysis of GAD antibodies in combination with antibodies to IA-2 indicated that the group most resistant to cyclosporin were IA-2 antibody positive, GAD antibody negative. CONCLUSIONS: The results demonstrate that IA-2 antibody analysis is valuable in identifying individuals for whom immunosuppressive treatment would be most effective.
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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 | 0.002 |
| 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".