Risk of Foot Ulcer and Lower-Extremity Amputation Among Participants in the Diabetes Control and Complications Trial/Epidemiology of Diabetes Interventions and Complications Study
Bibliographic record
Abstract
OBJECTIVE: Intensive glycemic control reduces the risk of kidney, retinal, and neurologic complications in type 1 diabetes (T1D), but whether it reduces the risk of lower-extremity complications is unknown. We examined whether former intensive versus conventional glycemic control among Diabetes Control and Complications Trial (DCCT) participants with T1D reduced the long-term risk of diabetic foot ulcers (DFUs) and lower-extremity amputations (LEAs) in the subsequent Epidemiology of Diabetes Interventions and Complications (EDIC) study. RESEARCH DESIGN AND METHODS: DCCT participants (n = 1,441) completed 6.5 years on average of intensive versus conventional diabetes treatment, after which 1,408 were enrolled in EDIC and followed annually over 23 years for DFU and LEA occurrences by physical examination. Multivariable Cox proportional hazard regression models estimated associations of DCCT treatment assignment and time-updated exposures with DFU or LEA. RESULTS: Intensive versus conventional glycemic control was associated with a significant risk reduction for all DFUs (hazard ratio 0.77 [95% CI 0.60, 0.97]) and a similar magnitude but nonsignificant risk reduction for first-recorded DFUs (0.78 [0.59, 1.03]) and first LEAs (0.70 [0.36, 1.36]). In adjusted Cox models, clinical neuropathy, lower sural nerve conduction velocity, and cardiovascular autonomic neuropathy were associated with higher DFU risk; estimated glomerular filtration rate <60 mL/min/1.73 m2, albuminuria, and macular edema with higher LEA risk; and any retinopathy and greater time-weighted mean DCCT/EDIC HbA1c with higher risk of both outcomes (P < 0.05). CONCLUSIONS: Early intensive glycemic control decreases long-term DFU risk, the most important antecedent in the causal pathway to LEA.
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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) | 0.001 | 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".