Visual impairment and mortality in patients with type 2 diabetes
Bibliographic record
Abstract
Objective: To evaluate whether visual acuity impairment was an independent predictor of mortality in patients with type 2 diabetes. Research design and methods: This is a 19-year follow-up of a cohort of 1241 patients newly diagnosed with type 2 diabetes and aged 40 years or over. Visual acuity was assessed by practicing ophthalmologists both at diabetes diagnosis and after 6 years. The logarithmic value of the visual acuity (logMAR) was the exposure. Multivariable Cox regression models were adjusted for multiple potential confounders including cardiovascular disease, and censored for potential mediators, that is, fractures/trauma. Primary outcomes were from national registers: all-cause mortality and diabetes-related mortality. Results: Visual impairment at diabetes diagnosis was robustly associated with subsequent 6-year all-cause mortality. Per 1 unit reduced logMAR acuity the incidence rate of all-cause mortality increased with 51% (adjusted HR: 1.51; 95% CI 1.12 to 2.03) and of fractures/trauma with 59% (HR: 1.59; 95% CI 1.18 to 2.15), but visual acuity was not associated with diabetes-related mortality. After censoring for fractures/trauma, visual acuity was still an independent risk factor for all-cause mortality (HR: 1.68; 95% CI 1.23 to 2.30). In contrast, visual acuity 6 years after diabetes diagnosis was not associated with the subsequent 13 years' incidence of any of the outcomes, as an apparent association with all-cause mortality and diabetes-related mortality was explained by confounding from comorbidity. Conclusions: Visual acuity measured by ophthalmologists in patients newly diagnosed with type 2 diabetes was an independent predictor of mortality in the short term.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".