Improving Prediction of Risk for the Development of Type 1 Diabetes—Insights From Populations at High Risk
Bibliographic record
Abstract
Prevention of type 1 diabetes has been brought closer to reality through the ability to identify populations at increased risk and through therapies that can modify the disease course (1). Identification of those who are at risk is based on the detection of diabetes-related autoantibodies (Ab) that can be found many years before clinical diagnosis. Those identified as being at increased risk due to having a family member who lives with type 1 diabetes or who have an increased genetic risk based on high-risk HLA have been followed in a number of longitudinal cohort studies (2–8) The preclinical phase of type 1 diabetes is described as progression from genetic susceptibility and immune activation to the development of single and then multiple Ab, to early abnormalities of glucose tolerance, and finally to clinical diabetes (9). Those who are found to have multiple Ab almost inevitably progress to diabetes (10). However, the time from detection of multiple Ab to diagnosis varies widely. To move the goal of the prevention of type 1 diabetes from research to clinical reality, refinements of strategies to predict progression to diabetes are needed. Anand et al. (11) and Bonifacio et al. (12), in two articles in this issue of Diabetes Care , help to define characteristics of Ab development and diabetes progression in >24,000 children at risk. The work by Anand et al. brings together data from five cohorts with a focus on the risk associated with the age at autoantibody (Ab) development, the number of Ab, and the HLA-DR-DQ genotype. The >16,000 children in these cohorts came from Germany, Sweden, Finland, Washington state, and Colorado and were tested for Ab before the age of 2.5 years and then followed …
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| 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".