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Record W4409252004 · doi:10.1136/jitc-2024-011049

Consensus disease definitions for ophthalmic immune-related adverse events of immune checkpoint inhibitors

2025· article· en· W4409252004 on OpenAlexaff
Eileen Chang, Renee Liu, Kiandokht Keyhanian, Katie Huynh, Meghan Berkenstock, M. Tariq Bhatti, John J. Chen, James Chodosh, Fiona Costello, Lauren A. Dalvin, Lindsey B. De Lott, Marc Dinkin, Robert A. Egan, Suzanne K. Freitag, Sapna Gangaputra, Lynn K. Gordon, Amanda C. Guidon, Douglas B. Johnson, Ninani Kombo, Michal Kramer, Andrew G. Lee, Michaël Levy, Anne-Marie Lobo-Chan, Dimosthenis Mantopoulos, George N. Papaliodis, Misha Pless, Julia Pimkina, Krista M. Rubin, H. Nida Sen, Afreen Shariff, Prem S. Subramanian, Edmund Tsui, Michael K. Yoon, Jon McDunn, Johnathan Rine, Kerry L. Reynolds, Lucia Sobrin, Bart K. Chwalisz

Bibliographic record

VenueJournal for ImmunoTherapy of Cancer · 2025
Typearticle
Languageen
FieldMedicine
TopicCancer Immunotherapy and Biomarkers
Canadian institutionsUniversity of Calgary
FundersProject Data Sphere
KeywordsMedicineUveitisDelphi methodAdverse effectFamily medicineDiseaseUsabilityOphthalmologyInternal medicineArtificial intelligenceComputer science

Abstract

fetched live from OpenAlex

Ophthalmic immune-related adverse events (Eye-irAEs) from immune checkpoint inhibitors can cause visual morbidity. The absence of standardized definitions for Eye-irAEs not only impedes the development of evidence-based treatments but also progress in translational research. The objective of this study was to develop consensus guidance for an approach to Eye-irAEs.Four ophthalmic physicians (uveitis specialists and neuro-ophthalmologists) drafted Eye-irAE consensus guidance and definitions, which were reviewed by the multidisciplinary Eye-irAE definition panel. The panel was divided into Group A (Neuro-ophthalmology/Orbital Disease) and Group B (Uveitis/Ocular Surface Disease). A modified Delphi consensus process was used, with two rounds of anonymous ratings by panelists and two meetings to discuss areas of controversy. For each disorder, five diagnostic components were evaluated: symptoms, examination findings, laboratory studies/imaging findings, diagnostic criteria, and treatment. Panelists rated content for usability, appropriateness and accuracy on 9-point scales in electronic surveys and provided free-text comments. Aggregated survey responses were incorporated into revised definitions. Consensus was based on numeric ratings using the RAND Corporation/ University of California Los Angeles Health Services Utilization Study (RAND/UCLA) Appropriateness Method with prespecified definitions.29 panelists from 25 academic medical centers voted on 114 rating scales (66 neuro-ophthalmic/orbital disease components, 48 uveitis/ocular surface disease components); of these, 86.3% (57/66) in Group A and 89.6% (43/48) in Group B reached first-round consensus. After revisions, all items except 6.1% (4/66) in Group A and 1.6% (1/60) in Group B received second-round consensus. Consensus definitions were achieved for 10/11 neuro-ophthalmic/orbital disorders: optic neuritis, inflammatory optic disc edema, arteritic ischemic optic neuropathy, optic perineuritis, orbital inflammation, thyroid eye disease-like orbital inflammation, cavernous sinus syndrome, oculomotor mononeuritis, trochlear mononeuritis, and abducens mononeuritis. Consensus definitions were achieved for 9/10 uveitis/ocular surface disorders: anterior uveitis, intermediate uveitis, posterior uveitis, panuveitis, Vogt-Koyanagi-Harada-like syndrome, sarcoidosis-like syndrome, acute macular neuroretinopathy, dry eye disease, and scleritis.These disease definitions establish a standardized classification for Eye-irAE, highlighting differences between irAEs and other inflammatory disorders. Importantly, diagnostic certainty does not always align directly with the need to treat as an Eye-irAE. Given the consensus from this representative panel group, it is anticipated the definitions will be used broadly across clinical and research settings.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.294
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.002
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 head0.025
GPT teacher head0.336
Teacher spread0.311 · 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 designBench or experimental
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

Citations16
Published2025
Admission routes1
Has abstractyes

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