Development of Autoantibodies in the TrialNet Natural History Study
Bibliographic record
Abstract
OBJECTIVE: Understanding the relationship between age and islet autoantibody (Ab) seroconversion can establish the optimal screening interval(s) to assess risk for type 1 diabetes, identify subjects who can participate in prevention trials, and determine associated costs. This study assessed the rates of seroconversion to glutamic acid decarboxylase positive (GAD65(+)), insulin positive (mIAA(+)), and insulinoma-associated protein 2 positive (ICA512(+)) in a large cohort of relatives of type 1 diabetes probands undergoing Ab rescreening in the TrialNet Natural History Study. RESEARCH DESIGN AND METHODS: Of 32,845 children aged <18 years screened for Abs, 1,287 (3.9%) were GAD65(+), 778 (2.4%) were mIAA(+), 677 (2.1%) were ICA512(+), and 31,038 were Ab-negative. Ab-negative children were offered annual rescreening up to 18 years of age. Cox regression was used to estimate the risk for GAD65, mIAA, and ICA512 seroconversion. RESULTS There were 205 children who seroconverted to GAD65(+), 155 who seroconverted to mIAA(+), and 53 who seroconverted to ICA512(+) over 5.8 years of follow-up. The risk of mIAA (hazard ratio 0.89 [95% CI 0.85-0.92]) and GAD65 (0.96 [0.93-0.99]) seroconversion significantly decreased with increasing age (i.e., for each 1-year increase in age, the risk of seroconversion decreased by 11% [P < 0.0001] for mIAA and 4% [P = 0.04] for GAD65) across all ages. The cumulative Ab seroconversion was 2% for those <10 years of age versus 0.7% for those ≥10 years of age. CONCLUSIONS: The risk of development of islet Abs declines with increasing age in type 1 diabetes relatives. These data support annual screening for children <10 years of age and one additional screening in adolescence.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".