A systematic review of risk factors for cataract in type 2 diabetes
Bibliographic record
Abstract
Type 2 diabetes (T2D) is a risk factor for cataract development. With T2D prevalence increasing, the burden of cataract-associated vision loss will also increase. We aimed to characterise cataract diabetes-specific risk factors to assist prevention and management strategies. As part of a systematic review, two investigators independently searched online electronic databases according to a predetermined protocol for relevant published data to end-March 2018. Studies were included if they were longitudinal with ≥100 participants, diabetes was defined, a description of cataract assessment was provided, data were from humans, and the reports were in English. Study quality was assessed using the Newcastle Ottawa Scale and GRADE. Of 5255 publications identified, 19 from 13 study populations were included. The overall risk of bias was low. There was between-study variability. Age and glycaemic control were consistently associated with cataract development in T2D, but blood pressure, diabetes duration, sex, and aspirin use were not. Serum lipids and smoking remain possible risk factors, but available data are inconclusive. Glycaemia is the only consistent modifiable risk factor amongst a range of candidate variables. Due to the lack of consistency of the available evidence, and since mortality associated with T2D is declining with the likelihood of increased cataract-associated vision loss, additional well-conducted longitudinal studies are needed to identify modifiable risk factors that could prevent or delay cataract formation.
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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 | 0.008 | 0.034 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.008 |
| Bibliometrics | 0.010 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".