353-OR: Determining the Sequence of Microvascular Complications: Results of Multistate Markov Modelling in the Diabetes Control and Complications Trial (DCCT)
Bibliographic record
Abstract
Screening for neuropathy is complex and underperformed in practice. If neuropathy rarely occurs first, screening for it could be delayed until retinopathy or nephropathy are documented. To address this, we aimed to definitively determine the sequence of microvascular complications in the natural history of type 1 diabetes (T1D) . Using data available from the public NIDDK Repository (1983-2012) , independent of the DCCT/EDIC research group, we performed a multistate analysis using biostatistics techniques for complex trivariate processes of the 1441 participants with T1D. Retinopathy (‘Eye’, E) , nephropathy (‘Kidney’, K) , and neuropathy (‘Nerve’, N) were each screened repeatedly and defined by the early-stage phenotypes (Figure 1) . Our model accounted for intermittent ascertainment and differing screening schedules. At baseline, participants had mean age 27±7 years and duration 6±4 years. Markov model simulations started in the absence of complications (Ø) . Eye was the most common initial complication (49%) , followed by nerve (28%) , and kidney (22%) . Median time to first complication was 5.3 years. 4% had no complications through follow-up. While retinopathy is conclusively most common, the high frequency of other initial complications reinforces the current practice of unselected early screening for all three, including neuropathy. Disclosure L.Lovblom: None. L.Briollais: None. G.Tomlinson: None. B.A.Perkins: Advisory Panel; Abbott Diabetes, Insulet Corporation, Sanofi, Board Member; Novo Nordisk, Other Relationship; Abbott Diabetes, Insulet Corporation, Medtronic, Novo Nordisk, Research Support; BMO Bank of Montreal, Novo Nordisk. Funding The Canadian Institutes of Health Research and the public NIDDK Central Repository.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".