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Record W2106449730 · doi:10.1258/jrsm.97.4.166

Treatment of age-related macular degeneration

2004· article· en· W2106449730 on OpenAlexaff
Andrew C. Browning, Winfried M. K. Amoaku, Harminder S. Dua

Bibliographic record

VenueJournal of the Royal Society of Medicine · 2004
Typearticle
Languageen
FieldMedicine
TopicRetinal Diseases and Treatments
Canadian institutionsQueen's University
Fundersnot available
KeywordsMacular degenerationDegeneration (medical)Computer scienceMedicineRetinal degenerationOphthalmologyWorld Wide WebOptometryRetinal

Abstract

fetched live from OpenAlex

Age-related macular degeneration (ARMD) is the most common cause of irreversible central visual loss in the elderly populations of the industrialized world,1-3 and will place increasing demands on health services as people live longer. For many years, the only treatment proven to be effective in arresting the progression of the wet form of ARMD was laser photocoagulation.4 Even then, only a small proportion of patients within a specific disease subgroup have been shown to benefit from this treatment, and the rate of disease recurrence (paradoxically often under the fovea) is high.5 Lately, researchers have developed novel strategies based on improved understanding of the pathophysiology, and this article summarizes 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)0.000
metaresearch head score (Gemma)0.000
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 score0.006
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.002

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 head0.015
GPT teacher head0.281
Teacher spread0.266 · 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

Citations7
Published2004
Admission routes1
Has abstractyes

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