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The KORA‐AGE eye study: Eye diseases in the elderly

2013· article· en· W2085191692 on OpenAlexaff
Jochen Graw, Ralf Strobl, Margit Heier, Birgit Linkohr, Annette Peters, Rolf Holle, Eva Grill

Bibliographic record

VenueActa Ophthalmologica · 2013
Typearticle
Languageen
FieldMedicine
TopicGlaucoma and retinal disorders
Canadian institutionsInstitute of Health Economics
Fundersnot available
KeywordsCataractsMedicineGlaucomaChristian ministryOphthalmologyMacular degenerationPopulationCataract surgeryEye diseaseOptometry

Abstract

fetched live from OpenAlex

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)

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.029
Threshold uncertainty score0.469

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.022
GPT teacher head0.301
Teacher spread0.279 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2013
Admission routes1
Has abstractyes

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