A New Approach for Diagnosing Type 1 Diabetes in Autoantibody-Positive Individuals Based on Prediction and Natural History
Bibliographic record
Abstract
OBJECTIVE: We assessed whether type 1 diabetes (T1D) can be diagnosed earlier using a new approach based on prediction and natural history in autoantibody-positive individuals. RESEARCH DESIGN AND METHODS: Diabetes Prevention Trial-Type 1 (DPT-1) and TrialNet Natural History Study (TNNHS) participants were studied. A metabolic index, the T1D Diagnostic Index60 (Index60), was developed from 2-h oral glucose tolerance tests (OGTTs) using the log fasting C-peptide, 60-min C-peptide, and 60-min glucose. OGTTs with Index60 ≥2.00 and 2-h glucose <200 mg/dL (Ind60+Only) were compared with Index60 <2.00 and 2-h glucose ≥200 mg/dL (2hglu+Only) OGTTs as criteria for T1D. Individuals were assessed for C-peptide loss from the first Ind60+Only OGTT to diagnosis. RESULTS: Areas under receiver operating characteristic curves were significantly higher for Index60 than for the 2-h glucose (P < 0.001 for both DPT-1 and the TNNHS). As a diagnostic criterion, sensitivity was higher for Ind60+Only than for 2hglu+Only (0.44 vs. 0.15 in DPT-1; 0.26 vs. 0.17 in the TNNHS) OGTTs. Specificity was somewhat higher for 2hglu+Only OGTTs in DPT-1 (0.97 vs. 0.91) but equivalent in the TNNHS (0.98 for both). Positive and negative predictive values were higher for Ind60+Only OGTTs in both studies. Postchallenge C-peptide levels declined significantly at each OGTT time point from the first Ind60+Only OGTT to the time of standard diagnosis (range -22 to -34% in DPT-1 and -14 to -27% in the TNNHS). C-peptide and glucose patterns differed markedly between Ind60+Only and 2hglu+Only OGTTs. CONCLUSIONS: An approach based on prediction and natural history appears to have utility for diagnosing T1D.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 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) | 0.001 | 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".