Poor glycemic control is associated with neuroretinal dysfunction in short-wavelength colour pathways in adolescents with type 1 diabetes
Bibliographic record
Abstract
Purpose: Poor glycemic control is a strong risk factor for diabetic retinopathy (DR). The purpose of this study is to identify if neuroretinal changes in short-wavelength colour pathways are potential biomarkers for DR in adolescents with type 1 diabetes and no ophthalmoscopic evidence of DR. Methods: Short-wavelength electroretinograms (sERGs) targeting short-wavelength colour pathways were recorded using blue flashes (peak 410nm) against an amber (594nm) background in 21 patients with type 1 diabetes (16 ± 1.78 years) and 19 controls (17 ± 3.84 years). The outcome measures were implicit time of the b-wave and the amplitude of the photopic negative response (PhNR). These measures represent inner and middle retinal responses respectively. Hemoglobin A1c (HbA1c) values, a measure of long-term glycemic control, were collected closest to the time of testing. Multiple linear regression analyses were conducted to assess the association of HbA1c with each sERG outcome measure while controlling for duration of diabetes. Results: sERG b-wave implicit times were significantly delayed (p=0.0015) and PhNR amplitudes were reduced (p=0.0021) in patients with type 1 diabetes when compared with controls. A multiple regression analysis with PhNR amplitudes in the patient group resulted in a significant association with HbA1c (r=0.57, p=0.004). sERG b-wave implicit times were not associated with HbA1c. Conclusion: Poor glycemic control is associated with neuroretinal dysfunction in short-wavelength colour pathways in adolescents with type 1 diabetes. This dysfunction was found in the inner retina specifically. Inner retinal function in short-wavelength colour pathways may be a potential biomarker for DR progression.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".