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Record W4385897960 · doi:10.1186/s12348-023-00342-5

Challenges in posterior uveitis—tips and tricks for the retina specialist

2023· review· en· W4385897960 on OpenAlexaff
Manuel Paez-Escamilla, Sonny Caplash, Gagan Kalra, Jamie L. Odden, Danielle Price, Oscar C. Marroquin, Stephen Koscumb, Patrick W. Commiskey, Chad Indermill, Jerome Finkelstein, Anna G. Gushchin, Andreea Coca, Thomas R. Friberg, Andrew W. Eller, Denise Gallagher, Jean C. Harwick, Evan L. Waxman, Jay Chhablani, Gabrielle R. Bonhomme, Colin Prensky, Alexander J. Anetakis, Joseph N. Martel, Erika Massicotte, Raphaëlle Orès, Jean‐François Girmens, Thomas M. Pearce, José‐Alain Sahel, Kunal K. Dansingani, Mark Westcott, Marie‐Hélène Errera

Bibliographic record

VenueJournal of Ophthalmic Inflammation and Infection · 2023
Typereview
Languageen
FieldMedicine
TopicOcular Diseases and Behçet’s Syndrome
Canadian institutionsMcGill University
FundersNational Eye InstituteNational Institutes of HealthResearch to Prevent BlindnessEye and Ear Foundation of Pittsburgh
KeywordsMedicineOphthalmologyRetinaOptometryUveitisNeurosciencePsychology

Abstract

fetched live from OpenAlex

PURPOSE: Posterior uveitis is a common chorioretinal pathology affecting all ages worldwide and is a frequent reason for referral to the retina clinic. The spectrum of etiologies for uveitis is very broad and includes infectious and auto-immune diseases. Inflammation can be confined to the eye or may be a part of systemic disease. A useful outline is therefore proposed to aid in the correct diagnosis of these challenging entities. The situation is further complicated by the fact that many neoplastic conditions resemble features of posterior uveitis; they are known as "masqueraders of uveitis". Here, we summarize different posterior uveitides that present with rare findings, along with masqueraders that can be difficult to distinguish. These conditions pose a diagnostic dilemma resulting in delay in treatment because of diagnostic uncertainty. METHODS: An extensive literature search was performed on the MEDLINE/PUBMED, EBSCO and Cochrane CENTRAL databases from January 1985 to January 2022 for original studies and reviews of predetermined diagnoses that include posterior uveitic entities, panuveitis and masquerade syndromes. RESULTS: We described conditions that can present as mimickers of posterior uveitis (i.e., immune check-points inhibitors and Vogt-Koyanagi-Harada-like uveitis; leukemia and lymphoma associated posterior uveitis), inflammatory conditions that present as mimickers of retinal diseases (i.e., Purtscher-like retinopathy as a presentation of systemic lupus erythematosus; central serous chorioretinopathy masquerading inflammatory exudative retinal detachment), and uveitic conditions with rare and diagnostically challenging etiologies (i.e., paradoxical inflammatory effects of anti-TNF-α; post vaccination uveitis; ocular inflammation after intravitreal injection of antiangiogenic drugs). CONCLUSION: This review of unique posterior uveitis cases highlights the overlapping features of posterior uveitis (paradoxical inflammatory effects of anti -TNF α and uveitis; Purtscher-like retinopathy as a presentation of systemic lupus erythematosus, …) and the nature of retinal conditions (ischemic ocular syndrome, or central retinal vein occlusion, amyloidosis, inherited conditions like retinitis pigmentosa, autosomal dominant neovascular inflammatory vitreoretinopathy (ADNIV), etc.…) that may mimic them is represented. Careful review of past uveitis history, current medications and recent vaccinations, detailed examination of signs of past or present inflammation, eventually genetic testing and/ or multimodal retinal imaging (like fluorescein angiography, EDI-OCT, OCT-angiography for lupus Purtscher-like retinopathy evaluation, or ICG for central serous retinopathy, or retinal amyloid angiopathy) may aid in correct diagnosis.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.989
Threshold uncertainty score0.483

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 integrity0.0000.000
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 head0.144
GPT teacher head0.381
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 designOther design
Domainnot available
GenreReview

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

Citations14
Published2023
Admission routes1
Has abstractyes

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