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Record W2469056629 · doi:10.1159/000446819

Genetic Risk Evaluation in Wet Age-Related Macular Degeneration Treatment Response

2016· article· en· W2469056629 on OpenAlexaff
Varun Chaudhary, Michael H. Brent, Wai‐Ching Lam, Robert G. Devenyi, Joshua C. Teichman, Michael Mak, Joshua Barbosa, Harneel Kaur, Ronald Carter, Forough Farrokhyar

Bibliographic record

VenueOphthalmologica · 2016
Typearticle
Languageen
FieldMedicine
TopicRetinal Diseases and Treatments
Canadian institutionsUniversity of TorontoMcMaster UniversitySt. Joseph’s Healthcare Hamilton
Fundersnot available
KeywordsMacular degenerationOphthalmologyMedicineBiologyOptometry

Abstract

fetched live from OpenAlex

<b><i>Objective:</i></b> To evaluate the pharmacogenetic relationship between <i>CFH</i> haplotypes and single nucleotide polymorphisms (SNPs) with response to ranibizumab treatment for neovascular age-related macular degeneration (nAMD). <b><i>Patients and Methods:</i></b> This was a prospective cohort study involving 70 treatment-naive nAMD patients. Patients were genotyped for <i>CFH</i> haplotypes and SNPs in the <i>C3, ARMS2, </i>and <i>mtDNA </i>genes. Visual acuity and central macular thickness were assessed at baseline and during 6 monthly follow-up visits. Multivariate logistic regression was used to determine the association between genotypes and a gain of ≥15 letters at the 6-month endpoint after adjusting for potential confounders. <b><i>Results:</i></b><i>CFH</i> haplotypes were associated with a gain of ≥15 letters at the 6-month endpoint (p = 0.046). Patients expressing protective haplotypes were more likely to achieve a gain of ≥15 letters relative to the greatly increased risk haplotypes [OR 6.58 (95% CI: 1.37, 31.59)]. <b><i>Conclusion:</i></b><i>CFH</i> is implicated in nAMD patient treatment response to ranibizumab.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.094
Threshold uncertainty score1.000

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)0.0010.000

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 head0.043
GPT teacher head0.332
Teacher spread0.289 · 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 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

Citations10
Published2016
Admission routes1
Has abstractyes

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