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Record W2809503486 · doi:10.1016/j.ajoc.2018.06.004

A diagnosis of Stevens-Johnson Syndrome (SJS) in a patient presenting with superficial keratitis

2018· article· en· W2809503486 on OpenAlexaff
Forson Chan, Matthew D. Benson, David J.A. Plemel, Muhammad N. Mahmood, Stanley M. H. Chan

Bibliographic record

VenueAmerican Journal of Ophthalmology Case Reports · 2018
Typearticle
Languageen
FieldMedicine
TopicDrug-Induced Adverse Reactions
Canadian institutionsUniversity of AlbertaUniversity of British Columbia
Fundersnot available
KeywordsMedicinePhotophobiaDermatologyKeratitisSore throatCorneal abrasionMedical historyTopical steroidVisual acuitySurgeryOphthalmologyCornea

Abstract

fetched live from OpenAlex

PURPOSE: To describe a case of Stevens-Johnson syndrome (SJS) diagnosed in a patient presenting with primarily ocular findings where SJS had not been initially suspected. OBSERVATIONS: A 23-year-old female presented with a 2 day history of bilateral eye pain, conjunctival injection, decreased visual acuity, and photophobia in the context of a 4 day history of fever, headache, and sore throat. She was found to have bilateral superficial keratitis and treated for suspected early infectious keratitis secondary to extended contact lens wear. She returned the next day with worsening visual symptoms, a new macular rash over her upper torso, and new ulcerating lesions over her buccal and perioral tissue. The patient was diagnosed with SJS. She was successfully treated using systemic cyclosporine with antibiotics and steroid eye drops. CONCLUSIONS AND IMPORTANCE: Ophthalmologists may be the first physicians to diagnose SJS, a life-threatening condition that can initially present with non-specific viral prodromal symptoms and ocular signs alone. This case emphasizes the importance of considering a patient's entire clinical history, especially when the presentation is atypical and the diagnosis is not obviously apparent.

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)0.000
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: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.130
Threshold uncertainty score0.602

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
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 head0.017
GPT teacher head0.296
Teacher spread0.279 · 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 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

Citations8
Published2018
Admission routes1
Has abstractyes

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