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Record W4400345718 · doi:10.1097/icb.0000000000001619

SYSTEMIC ANTI-VEGF BIOSIMILAR THERAPY ASSOCIATED WITH IMPROVED MACULAR ANATOMY AND DURATION OF EFFECT IN A PATIENT WITH NEARLY RECALCITRANT NEOVASCULAR AGE-RELATED MACULAR DEGENERATION

2024· article· en· W4400345718 on OpenAlexaff
Kipp R Morgan, Paige Richards, Jonathan S. Chang

Bibliographic record

VenueRetinal Cases & Brief Reports · 2024
Typearticle
Languageen
FieldMedicine
TopicRetinal Diseases and Treatments
Canadian institutionsCanadian Research Institute for the Advancement of Women
FundersResearch to Prevent Blindness
KeywordsMedicineMacular degenerationOphthalmologyBiosimilarRanibizumabSurgeryBevacizumabInternal medicine

Abstract

fetched live from OpenAlex

PURPOSE: To present a patient with neovascular AMD treated systemically with the biosimilar bevacizumab-awwb (Mvasi) with superior subretinal fluid resolution when compared with continuous and repeated intravitreal treatments. METHODS: Retrospective single case report. RESULTS: After 3 years of monthly aflibercept treatment for nAMD, the patient had persistent subretinal fluid. Systemic bevacizumab-awwb (Mvasi) infusions were initiated by her oncologist for ovarian cancer, and subretinal fluid resolved for the first time. After one additional aflibercept injection and continued bevacizubam-awwb infusions for her cancer, subretinal fluid did not recur. Thirteen weeks later, at the final follow-up before the patient passed away, the macula remained dry and no additional intravitreal treatment was given. CONCLUSION: When systemic anti-VEGF biosimilar therapy was administered, there was improved anatomy and prolonged duration of effect compared with the intravitreal therapy alone. Adverse systemic effects limit the routine use of systemic anti-VEGF therapy for retinal disease. However, if a patient requires systemic anti-VEGF or anti-VEGF biosimilar therapy for malignancy, it may also benefit retinal disease leading to benefits in quality of life and fewer office visits.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.352
Threshold uncertainty score0.941

Codex and Gemma teacher scores by category

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

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 head0.006
GPT teacher head0.243
Teacher spread0.237 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations0
Published2024
Admission routes1
Has abstractyes

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