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Record W3168378710 · doi:10.1080/01676830.2021.1939726

Acute orbital inflammation with loss of vision: a paradoxical adverse event associated with infliximab therapy for Crohn’s disease

2021· article· en· W3168378710 on OpenAlexaff
David R. Jordan, John S.Y. Park, Danah Albreiki

Bibliographic record

VenueOrbit · 2021
Typearticle
Languageen
FieldMedicine
TopicOcular Diseases and Behçet’s Syndrome
Canadian institutionsOttawa HospitalUniversity of Ottawa
Fundersnot available
KeywordsMedicineInfliximabAdalimumabEtanerceptAdverse effectScleritisInflammatory bowel diseaseRetinal vasculitisOptic neuritisExacerbationDermatologyOptic neuropathyUveitisDiseaseVasculitisInternal medicineOptic nerveImmunologyOphthalmologyTumor necrosis factor alphaMultiple sclerosis

Abstract

fetched live from OpenAlex

Anti-TNF-α agents (e.g. infliximab, adalimumab, etanercept) are effective management options in various inflammatory and autoimmune diseases (e.g. inflammatory bowel disease). The occurrence during anti-TNF-α agent therapy of a new onset or exacerbation of an inflammatory condition that usually responds to this class of drug has been termed a paradoxical adverse event (PAE). A wide range of ophthalmic PAEs have been reported including uveitis, optic neuritis/neuropathy, scleritis, orbital myositis, retinal vasculitis, and others. The patient reported herein developed a dramatic orbital inflammatory PAE during his infliximab infusions, which manifested as an acute orbital apex syndrome with vision loss. Physicians using this medication should be aware of this serious vision-threatening PAE, and urgent therapy with high dose intravenous corticosteroids may be required.

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)0.003
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 score0.001
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0010.000

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 head0.009
GPT teacher head0.271
Teacher spread0.262 · 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
Published2021
Admission routes1
Has abstractyes

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