Validity and Reproducibility of Measurement of Islet Autoreactivity by T-Cell Assays in Subjects With Early Type 1 Diabetes
Bibliographic record
Abstract
OBJECTIVE: Type 1 diabetes results from an immunemediated destruction of beta-cells, likely to be mediated by T lymphocytes, but the sensitivity, specificity, and other measures of validity of existing assays for islet autoreactive T-cells are not well established. Such assays are vital for monitoring responses to interventions that may modulate disease progression. RESEARCH DESIGN AND METHODS: We studied the ability of cellular assays to discriminate responses in patients with type 1 diabetes and normal control subjects in a randomized blinded study in the U.S. and U.K. We evaluated the reproducibility of these measurements overall and to individual analytes from repeat collections. RESULTS: Responses in the cellular immunoblot, U.K.-ELISPOT, and T-cell proliferation assays could differentiate patients from control subjects with odds ratios of 21.7, 3.44, and 3.36, respectively, with sensitivity and specificity as high as 74 and 88%. The class II tetramer and U.S. ELISPOT assays performed less well. Despite the significant association of the responses with type 1 diabetes, the reproducibility of the measured responses, both overall and individual analytes, was relatively low. Positive samples from normal control subjects (i.e., false positives) were generally isolated to single assays. CONCLUSIONS: The cellular immunoblot, U.K.-ELISPOT, and T-cell proliferation assays can distinguish responses from patients with type 1 diabetes and healthy control subjects. The limited reproducibility of the measurements overall and of responses to individual analytes may reflect the difficulty in detection of low frequency of antigen-specific T-cells or variability in their appearance in peripheral blood.
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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 | 0.037 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".