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Record W4396229481 · doi:10.4103/joco.joco_21_23

Hypotony and Anterior Uveitis following Dual Therapy with Nivolumab and Ipilimumab for Metastatic Melanoma: A Case Report

2023· article· en· W4396229481 on OpenAlexaff
Nikhil S. Patil, David Dudok, Sarit Khimdas

Bibliographic record

VenueJournal of Current Ophthalmology · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMelanoma and MAPK Pathways
Canadian institutionsMcMaster University
Fundersnot available
KeywordsMedicineIpilimumabNivolumabMetastatic melanomaMelanomaUveitisDermatologyOncologyInternal medicineOphthalmologyImmunotherapyCancer researchCancer

Abstract

fetched live from OpenAlex

Purpose: To describe a rare case of hypotony and anterior uveitis following dual therapy with nivolumab and ipilimumab for metastatic melanoma. Methods: Case report. Results: Here, we present the case of a 64-year-old man taking nivolumab and ipilimumab dual therapy for BRAF+ (v-raf murine sarcoma viral oncogene homolog B1) metastatic melanoma. After treatment for 3 months, he presented to the ophthalmology clinic with bilateral intraocular pressures of 1 mmHg, bilateral keratic precipitates, cataracts, posterior synechiae, and anterior chamber inflammation. He improved with topical medications and the cessation of immunotherapy. Conclusions: Immunotherapies are a novel class of chemotherapy that has increased in prevalence for the treatment of numerous malignancies. There are many rare complications from these medications that are sparsely reported. Knowledge of ocular hypotony as a potential consequence of nivolumab and ipilimumab is important, particularly as it may arise months after treatment initiation and necessitate immunotherapy cessation.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0030.002
Science and technology studies0.0030.002
Scholarly communication0.0020.002
Open science0.0010.002
Research integrity0.0050.004
Insufficient payload (model declined to judge)0.0020.001

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 head0.046
GPT teacher head0.328
Teacher spread0.282 · 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 source (direct Gemma or distilled Codex), not a consensus.

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

Citations1
Published2023
Admission routes1
Has abstractyes

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