ASSOCIATIONS BETWEEN DYSGLYCEMIA, RETINAL NEURODEGENERATION, AND MICROALBUMINURIA IN PREDIABETES AND TYPE 2 DIABETES
Why this work is in the frame
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Bibliographic record
Abstract
PURPOSE: To explore the association between retinal neurodegeneration and metabolic parameters in progressive dysglycemia. METHOD: A cross-sectional study was performed on 68 participants: normal glucose tolerance (n = 23), prediabetes (n = 25), and Type 2 diabetes without diabetic retinopathy (n = 20). Anthropometric assessment and laboratory sampling for HbA1c, fasting glucose, insulin, c-peptide, lipid profile, renal function, and albumin-to-creatinine ratio were conducted. Central and pericentral macular thicknesses on spectral domain optical coherence tomography were compared with systemic parameters. RESULTS: Baseline demographic characteristics were similar across all groups. Cuzick's trend test revealed progressive full-thickness macular thinning with increasing dysglycemia across all three groups (P = 0.015). The urinary albumin-to-creatinine ratio was significantly correlated with full-thickness superior (R = -0.435; P = 0.0002), inferior (R = -0.409; P = 0.0005), temporal (R = -0.429; P = 0.003), and nasal (R = -0.493; P < 0.0001) pericentral macular thinning, after post hoc Bonferroni adjustment. There was no association between macular thinning and waist circumference, body mass index, blood pressure, lipid profile, or insulin resistance. CONCLUSION: Progressive dysglycemia is associated with macular thinning before the onset of visible retinopathy and occurs alongside microalbuminuria. Retinal neurodegenerative changes may help identify those most at risk from dysglycemic end-organ damage.
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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 | 0.000 | 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 it