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Record W1963683893 · doi:10.2174/187152507780363197

Current and Emerging Concepts in the Management of Neovascular Age-Related Macular Degeneration

2007· review· en· W1963683893 on OpenAlexaff
S. Maloney, K.D. Godeiro, Alexandre Nakao Odashiro, Miguel N. Burnier

Bibliographic record

VenueCardiovascular & Hematological Agents in Medicinal Chemistry · 2007
Typereview
Languageen
FieldMedicine
TopicRetinal Diseases and Treatments
Canadian institutionsMcGill University
Fundersnot available
KeywordsMacular degenerationChoroidal neovascularizationMedicineBlindnessChoroidDiseaseNeovascularizationPopulationPhotodynamic therapyDegeneration (medical)Intensive care medicineOphthalmologyAngiogenesisOptometryPathologyRetinaNeuroscienceInternal medicinePsychology

Abstract

fetched live from OpenAlex

Age-related macular degeneration (AMD) is the leading cause of blindness in the elderly worldwide. The more severe form of the disease, known as neovascular AMD, is characterized by aberrant growth of blood vessels from the choroid into the subretinal space. This pathologic choroidal neovascularization can have drastic consequences, often seriously impairing vision in affected individuals. Current treatment approaches focus on combination therapies that include photodynamic therapy in conjunction with numerous forms of antiangiogenic or anti-inflammatory drug intervention. To date, however, no adequate treatment is available for the majority of affected individuals. The threat of a rapidly aging population provides the impetus for aggressive efforts to control the prevalence and progression of this disease. This review will outline the currently available pharmacotherapies, discussing the justification for their use as well as their shortcomings. Furthermore, drugs that are currently under investigation as monotherapies and adjuncts will be highlighted. The potential for alternate targets will also be examined, with a focus on the most promising candidates.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.895
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.071
GPT teacher head0.389
Teacher spread0.318 · 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.

Study designOther design
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

Citations6
Published2007
Admission routes1
Has abstractyes

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