Novel Approach for Assessing Outcomes of Type 1 Diabetes Prevention Trials Over a Fixed Time Interval
Bibliographic record
Abstract
We evaluated whether a binary metabolic end point for change (Δ) from baseline to 1-year postrandomization could be useful in type 1 diabetes (T1D) prevention trials. Using 2-h oral glucose tolerance testing data from the stage 1 participants in the recent abatacept prevention trial and similar participants in the observational TrialNet Pathway to Prevention (PTP) study, we assessed Δmetabolic measures, plotted glucose and C-peptide response curves, and categorized vectors for Δ from baseline to 1 year as metabolic treatment failure versus success. Analyses were validated using the teplizumab prevention study. PTP participants with Δglucose >0 and ΔC-peptide <0 from baseline to 1 year were at substantially higher risk for stage 3 T1D than those with Δglucose <0 and ΔC-peptide >0 (P < 0.0001). Based on this, we compared placebo versus treatment groups in both trials for failure (Δglucose >0 with ΔC-peptide <0) versus success (Δglucose <0 with ΔC-peptide >0) after 1 year. Using this end point, a favorable metabolic impact of abatacept was found after 12 months of treatment. An analytic approach using a binary metabolic end point of failure versus success at a fixed time interval appears to detect treatment effects at least as well as standard primary end points with shorter follow-up. ARTICLE HIGHLIGHTS: Challenges in time to event type 1 diabetes (T1D) prevention trial design can yield negative results even for treatments that may actually improve disease pathology. We evaluated whether a binary metabolic end point for 12-month change from baseline to 1 year postrandomization could be useful in T1D prevention trials. This approach detected treatment effects at least as well as standard primary end points with shorter follow-up. Fixed interval metabolic end points should be used in combination with traditional T1D end points to better understand treatment effects of preventive agents.
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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 | 0.281 | 0.361 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.010 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 0.006 |
| 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".