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Record W4214551678 · doi:10.1080/09273948.2022.2042325

Bilateral Panuveitis with Occlusive Vasculitis following Coronavirus Disease 2019 Vaccination

2022· article· en· W4214551678 on OpenAlexaff
Mélanie Hébert, Simon Couture, Isabelle Schmit

Bibliographic record

VenueOcular Immunology and Inflammation · 2022
Typearticle
Languageen
FieldMedicine
TopicRetinal and Optic Conditions
Canadian institutionsCentre hospitalier universitaire de QuébecHôpital du Saint-SacrementUniversité Laval
Fundersnot available
KeywordsMedicineVasculitisEtiologyUveitisDermatologyVaccinationSurgeryAdverse effectOphthalmologyDiseasePathologyInternal medicine

Abstract

fetched live from OpenAlex

PURPOSE: To report a case of bilateral panuveitis and occlusive vasculitis following COVID-19 vaccination. STUDY DESIGN: Case report. RESULTS: A 41-year-old otherwise healthy male presented with progressive vision loss and floaters starting 48 hours after a first dose of COVID-19 vaccine. Examination initially showed bilateral anterior uveitis, but this evolved into bilateral panuveitis with occlusive vasculitis despite topical corticosteroids over two weeks. The patient underwent extensive testing for other etiologies which were excluded. He was successfully treated with a gradual taper of topical and systemic corticosteroids leading to improvement of signs and symptoms. Follow-up is maintained for observation of avascular zones with possible neovascularization which could require laser as needed. CONCLUSIONS: The temporal association between vaccine and presentation makes this a plausible etiology. This remains a rare adverse event, but clinicians should be aware of this possibility to include it in their differential diagnosis when confronted with idiosyncratic ocular presentations.

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)0.001
metaresearch head score (Gemma)0.003
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 score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0030.001

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 head0.008
GPT teacher head0.248
Teacher spread0.240 · 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

Citations9
Published2022
Admission routes1
Has abstractyes

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