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Record W4391825415 · doi:10.1136/bcr-2023-256860

Bilateral non-arteritic posterior ischaemic optic neuropathy following COVID-19 vaccination

2024· article· en· W4391825415 on OpenAlexaff
Dominique Salh, Ashlyn Pinto, Paul R. Freund

Bibliographic record

VenueBMJ Case Reports · 2024
Typearticle
Languageen
FieldMedicine
TopicRetinal and Optic Conditions
Canadian institutionsDalhousie University
Fundersnot available
KeywordsMedicineCoronavirus disease 2019 (COVID-19)Ophthalmology2019-20 coronavirus outbreakOptic neuropathySevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Optic nerveVirologyInternal medicineDiseaseOutbreak

Abstract

fetched live from OpenAlex

We report a case of bilateral posterior ischaemic optic neuropathy, which followed vaccination with ChAdOx1 nCoV-19 for COVID-19 prophylaxis. A man in his early 60s was initially assessed for bilateral acute vision loss following 3 days of frontal headaches. The patient denied any other preceding visual concerns or symptoms of giant cell arteritis. The patient received his first dose of the ChAdOx1 nCoV-19 vaccination 10 days before the onset of his symptoms.At initial presentation, visual acuity was counting fingers bilaterally. Blood work found normal erythrocyte sedimentation rate (6 mm/hour) and C reactive protein (<5 mg/L) as well as a negative infectious and autoimmune serology. He was negative for COVID-19 with PCR testing. Diffusion-weighted MRI showed restricted diffusion along both optic nerves. After 5 months, the patient's visual acuity remained counting fingers bilaterally with pale optic nerves. Isolated bilateral posterior ischaemic optic neuropathy has not been reported in association with the ChAdOx1 nCoV-19 vaccine.

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)0.004
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
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0020.002
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0040.003
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 head0.023
GPT teacher head0.347
Teacher spread0.324 · 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

Citations2
Published2024
Admission routes1
Has abstractyes

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