Long‐acting insulin analogues and the risk of diabetic retinopathy among patients with type 2 diabetes: A population‐based cohort study
Bibliographic record
Abstract
AIM: To determine whether the use of long-acting insulin analogues is associated with an increased risk of incident diabetic retinopathy (DR) among patients with type 2 diabetes. METHODS: Using data from the Clinical Practice Research Datalink Aurum, this retrospective, population-based cohort study included patients with type 2 diabetes who initiated a long-acting insulin analogue (glargine, detemir, degludec) or Neutral Protamine Hagedorn (NPH) insulin. The primary outcome was incident DR. We used Cox proportional hazards models with inverse probability of treatment weighting to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for incident DR with insulin analogues versus NPH insulin. RESULTS: There were 66 280 new users of long-acting insulin analogues and 66 173 new users of NPH insulin. The incidence rate of DR was 101.7 per 1000 person-years (95% CI, 98.7-104.8) for insulin analogues and 93.2 (95% CI, 90.0-96.5) per 1000 person-years for NPH insulin. Compared with the current use of NPH insulin, insulin analogues were not associated with the risk of incident DR (HR 1.04, 95% CI, 0.99-1.09). The adjusted HRs were 0.84 (95% CI, 0.66-1.07) for proliferative DR and 1.02 (95% CI, 0.97-1.08) for non-proliferative DR. CONCLUSIONS: Compared with NPH insulin, long-acting insulin analogues were not associated with the risk of incident DR among patients with type 2 diabetes. This finding provides important reassurance regarding the safety of long-acting insulin analogues with respect to incident DR.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".