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Record W4294091534 · doi:10.1159/000526548

Outcomes of Second-Line Intravitreal Anti-VEGF Switch Therapy in Radiation Retinopathy Secondary to Uveal Melanoma: Moving from Bevacizumab to Aflibercept

2022· article· en· W4294091534 on OpenAlexafffund
Ojas Srivastava, Ezekiel Weis

Bibliographic record

VenueOcular Oncology and Pathology · 2022
Typearticle
Languageen
FieldMedicine
TopicOcular Oncology and Treatments
Canadian institutionsUniversity of CalgaryUniversity of Alberta
FundersAlberta Innovates
KeywordsAfliberceptMedicineBevacizumabOphthalmologyChoroidal melanomaRanibizumabMelanomaSurgeryChemotherapyCancer research

Abstract

fetched live from OpenAlex

Introduction: Radiation retinopathy is a dose-dependent complication of the retina following exposure to ionizing radiation. The objective of this prospective case series is to determine the clinical efficacy of intravitreal aflibercept for radiation retinopathy secondary to radiotherapy for uveal melanoma in those that failed intravitreal bevacizumab treatment. Methods: A case series of 30 patients with a mean age of 57 ± 15 years with radiation retinopathy were enrolled. Visual acuity (VA) and central foveal thickness (CFT) responses to therapy were assessed with regression analyses at 1 month, 3 months, and 6 months following the switch to aflibercept. Results: Regression analyses showed a statistically significant reduction in CFT and improvements in VA following the switch to treatment by aflibercept at 1 month, 3 months, and 6 months. The mean CFT improved from 476 μm ± 170 to 386 μm ± 139 and the mean VA improved minimally from 20/115 ± 20/63 to 20/112 ± 20/54 over 6 months. After 6 months of aflibercept, 46% of patients displayed a CFT improvement of 100 μm or greater and 23% of patients showed improvement in VA of 1 line or better. Conclusion: This pilot study suggests that patients with radiation retinopathy who have failed monthly intravitreal bevacizumab may respond to aflibercept.

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 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 score0.136
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.013
GPT teacher head0.291
Teacher spread0.278 · 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

Citations5
Published2022
Admission routes2
Has abstractyes

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