Generalizability of progression risk in the TN-10 trial to a European population with or without a first-degree relative with type 1 diabetes
Bibliographic record
Abstract
Objective In the TrialNet 10 (TN-10) trial, teplizumab delayed onset of stage 3 type 1 diabetes in US and Canadian individuals with stage 2 disease who had a relative with type 1 diabetes. Here, the generalizability of the population risk in TN-10 to a European population with or without first-degree relatives (FDRs) with type 1 diabetes was investigated. Research Design and Methods This retrospective study used data from participants with stage 2 type 1 diabetes from the TN-10 placebo arm and the Fr1da population-based screening program in Germany (Fr1da group) to investigate time to progression from stage 2 to 3 type 1 diabetes. The study only had sufficient power to detect large differences. Results Risk of progression to stage 3 type 1 diabetes was comparable between the TN-10 placebo arm (n=32) and the Fr1da group (n=152; HR=1.3 [95% CI: 0.8-2.1]). Once prognostic factors significantly associated with progression in this study (anti-IA-2 antibodies, HbA1c >5.7%, and 120-minute OGTT) were included in the model, the adjusted HR was 1.1 (95% CI: 0.6-2.1). Fr1da group participants with (n=45) and without (n=107) FDRs with type 1 diabetes had similar time to progression to stage 3. Age-based subanalysis demonstrated minimal impact of age on progression time. Conclusions Time to progression to stage 3 appeared similar between the TN-10 placebo arm and the Fr1da group and between participants with and without FDRs with disease. Results suggest progression risk from the TN-10 trial may be generalizable to European populations with or without FDRs with type 1 diabetes.
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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 | 0.041 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".