The TrialNet Natural History Study of the Development of Type 1 Diabetes: objectives, design, and initial results
Bibliographic record
Abstract
OBJECTIVES: TrialNet's goal to test preventions for type 1 diabetes has created an opportunity to gain new insights into the natural history of pre-type 1 diabetes. The TrialNet Natural History Study (NHS) will assess the predictive value of existing and novel risk markers for type 1 diabetes and will find subjects for prevention trials. RESEARCH DESIGN AND METHODS: The NHS is a three-phase, prospective cohort study. In phase 1 (screening), pancreatic autoantibodies (glutamic acid decarboxylase, insulin, ICA-512, and islet cell antibodies) are measured. Phase 2 (baseline risk assessment) includes oral glucose tolerance tests (OGTTs) in antibody-positive subjects and estimation of 5-yr diabetes risks according to the OGTT and number of confirmed positive antibody tests. Phase 3 (follow-up risk assessments) requires OGTTs every 6 months. In phases 2 and 3, samples are collected for future tests of T-lymphocyte function, autoantibody isotypes, RNA gene expression, and proteomics. The primary outcome is diabetes onset. RESULTS: Of 12 636 relatives screened between March 2004 and December 2006, 605 (4.8%) were positive for at least one biochemical antibody. Of these, 322 were confirmed antibody positive and completed phase 2, of whom 296 subjects were given preliminary 5-yr diabetes risks of <25% (n = 132), > or =25% (n = 36), and > or =50% (n = 128) where the latter two categories represent different subjects based on number of confirmed positive antibodies (2, > or =25%; 3 or more, > or =50%) and/or an abnormal OGTT (> or =50%). CONCLUSIONS: The NHS is identifying potential prevention trial subjects and is assembling a large cohort that will provide new natural history information about pre-type 1 diabetes. Follow-up to diabetes will help establish the biological significance and clinical value of novel type 1 diabetes risk markers.
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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 | 0.011 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".