The KORA‐AGE eye study: Eye diseases in the elderly
Bibliographic record
Abstract
Abstract Purpose To estimate the prevalence of major age‐related eye diseases in a population‐based regional study in Southern Germany. Methods 822 randomly selected persons (age 68‐96 years) from the KORA AGE study were asked in 2012 in a standardized interview for the presence of major eye disorders like cataracts, glaucoma and age‐related macula degeneration (AMD). In case of any positive reply, the ophthalmologist in charge was asked for validation and specification of any eye disease. Results 465 persons reported any eye disorder (57%); 71% of them could be validated and specified. There were 182 confirmed cases of cataracts, 7 of glaucoma and 5 of AMD. Additionally, there were 52 cases of cataracts and AMD, also 54 cases of cataract and glaucoma and 11 cases of cataract, glaucoma and AMD. In 62% cases cataracts developed prior to any of the other eye diseases. Adjusted for age, women had a significantly higher risk for cataracts (OR = 1.72) and for AMD (OR = 1.94) than men; no gender‐specific difference was observed for glaucoma. Among patients with cataracts, 69% had lens surgery. Conclusion We confirmed cataracts as the major age‐related eye diseases; however, the number of glaucoma and AMD were surprisingly low. Further analyses are planned to identify risk factors and to show how eye diseases are independent risk factors for increased frailty and disability in the aged. This study was supported by the German Federal Ministry of Education and Research (BMBF) within the programme "Healthy Ageing" (FKZ 01ET1003)
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".