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Record W2027583618 · doi:10.1159/000066507

Common Eye Diseases of Elderly People: Identifying and Treating Causes of Vision Loss

2002· review· en· W2027583618 on OpenAlexaff
Patricia T. Harvey

Bibliographic record

VenueGerontology · 2002
Typereview
Languageen
FieldMedicine
TopicRetinal Diseases and Treatments
Canadian institutionsToronto Western Hospital
Fundersnot available
KeywordsMacular degenerationMedicineVerteporfinImpaired VisionBlindnessDiabetic retinopathyElderly peopleGlaucomaOptometryOlder peopleRetinopathyVisual impairmentOphthalmologyGerontologyDiabetes mellitusChoroidal neovascularizationPsychiatry

Abstract

fetched live from OpenAlex

Of the 38 million people who are blind, the majority, 22 million, are 60 years of age or older. The most common causes of vision loss in elderly people are age-related macular degeneration (AMD), cataract, glaucoma, and diabetic retinopathy. Of these, AMD is the leading cause of registered blindness in people over the age of 50 years in the western world. However, until recently, the treatment options for people with AMD have been severely limited. Verteporfin therapy is a new treatment that is efficacious and safe in selected patients with AMD who are at high risk of central vision loss. Physicians who are in regular contact with elderly people can help to minimize vision loss in this group of patients by being alert to the symptoms and signs of age-related eye diseases. This paper reviews each of the common eye diseases, with an emphasis on AMD because of the recent advances in treatment.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0030.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.003

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 head0.065
GPT teacher head0.406
Teacher spread0.341 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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

Citations77
Published2002
Admission routes1
Has abstractyes

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