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Record W4414057044 · doi:10.1159/000548216

Dramatic Inflammatory Regression of Choroidal Metastases from Renal Cell Carcinoma following Ipilimumab and Nivolumab Immunotherapy: A Case Series

2025· article· en· W4414057044 on OpenAlexaff
Kirk Stephenson, Katherine Paton

Bibliographic record

VenueOcular Oncology and Pathology · 2025
Typearticle
Languageen
FieldMedicine
TopicOcular Oncology and Treatments
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsNivolumabIpilimumabRenal cell carcinomaCollateral damageCarcinomaMelanoma

Abstract

fetched live from OpenAlex

Introduction: Renal cell carcinoma (RCC) is a rare cause of ophthalmic metastasis. Immune checkpoint blockers (ICBs) such as ipilimumab and nivolumab (ipi/nivo) are first-line therapies for advanced RCC. There are limited efficacy reports of ICBs for RCC choroidal metastases (CMs). Case One: A 43-year-old male with metastatic (lung) clear cell RCC presented with left eye scleritis and a 3.4 mm choroidal mass. One week after starting ipi/nivo, the lesion rapidly expanded to 11.9 mm with vitritis, subtotal exudative retinal detachment (ERD), and features of necrosis (heterogenous echogenicity). The lesion regressed over 10 months to 1.29 mm with resolution of ERD and improved visual acuity from counting fingers to 20/50. Case Two: A 63-year-old male with clear cell RCC presented with a right eye 7.2 mm choroidal mass and subretinal haemorrhage. The lesion enlarged to 10.9 mm with ERD and heterogenous echogenicity after starting ipi/nivo, which then regressed to 2.4 mm by 7 months, leaving retinal folds. Vision declined to hand motions and remained stable. Conclusion: Ipi/nivo can induce rapid and sustained regression of RCC CM but may cause profound intraocular inflammation, collateral damage to surrounding structures, and subsequent vision loss. This response may be enhanced in the presence of pre-existing scleritis.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.101
Threshold uncertainty score0.802

Codex and Gemma teacher scores by category

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

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 head0.008
GPT teacher head0.277
Teacher spread0.268 · 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
Published2025
Admission routes1
Has abstractyes

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