The Association of Skin Intrinsic Fluorescence With Type 1 Diabetes Complications in the DCCT/EDIC Study
Bibliographic record
Abstract
OBJECTIVE: To determine whether skin intrinsic fluorescence (SIF) is associated with long-term complications of type 1 diabetes (T1D) and, if so, whether it is independent of chronic glycemic exposure and previous intensive therapy. RESEARCH DESIGN AND METHODS: We studied 1,185 (92%) of 1,289 active Diabetes Control and Complications Trial (DCCT)/Epidemiology of Diabetes Interventions and Complications (EDIC) participants from 2010 to 2011. SIF was determined using a fluorescence spectrometer and related cross-sectionally to recently determined measures of retinopathy (stereo fundus photography), cardiac autonomic neuropathy (CAN; R-R interval), confirmed clinical neuropathy, nephropathy (albumin excretion rate [AER]), and coronary artery calcification (CAC). RESULTS: Overall, moderately strong associations were seen with all complications, before adjustment for mean HbA1c over time, which rendered these associations nonsignificant with the exception of sustained AER>30 mg/24 h and CAC, which were largely unaffected by adjustment. However, when examined within the former DCCT treatment group, associations were generally weaker in the intensive group and nonsignificant after adjustment, while in the conventional group, associations remained significant for CAN, sustained AER>30 mg/24 h, and CAC even after mean HbA1c adjustment. CONCLUSIONS: SIF is associated with T1D complications in DCCT\EDIC. Much of this association appears to be related to historical glycemic exposure, particularly in the previously intensively treated participants, in whom adjustment for HbA1c eliminates statistical significance.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".