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Record W2539801260 · doi:10.1016/j.ajoc.2016.10.009

Anterior infectious necrotizing scleritis secondary to Pseudomonas aeruginosa infection following intravitreal ranibizumab injection

2016· article· en· W2539801260 on OpenAlexaff
Razek Georges Coussa, Susan M. Wakil, Hady Saheb, David E. Lederer, Karin Oliver, Devinder Cheema

Bibliographic record

VenueAmerican Journal of Ophthalmology Case Reports · 2016
Typearticle
Languageen
FieldMedicine
TopicOcular Diseases and Behçet’s Syndrome
Canadian institutionsMcGill University Health Centre
FundersAllergan
KeywordsMedicineEndophthalmitisPseudomonas aeruginosaRanibizumabScleritisSurgeryAntibioticsOphthalmologyRegimenChemotherapyBevacizumabUveitis

Abstract

fetched live from OpenAlex

To report the occurrence and management of severe infectious scleritis in a 75 year-old woman following intravitreal ranibizumab injection. A 75 year-old monocular woman receiving monthly intravitreal ranibizumab injection for wet age related macular degeneration in the left eye presented with severe dull pain, decreased vision, and scleral melt with discharge 2 weeks after her last injection. The dilated fundus exam was devoid of vitritis. The patient was admitted to our hospital for both diagnostic and therapeutic purposes. She was initially started on aggressive oral and topical antibiotics, but showed no significant improvement. The scleral cultures were positive for Pseudomonas aeruginosa. In view of the aggressive nature of her infection, intravenous antibiotics were added to the treatment regimen. The patient recovered her baseline visual function after two weeks of intravenous, oral and, topical antibiotics. To our knowledge, this is the first case of anterior infectious necrotizing scleritis secondary to Pseudomonas aeruginosa infection following intravitreal ranibizumab injection. Clinicians performing intravitreal injections should have a high index of suspicion for iatrogenic infections including scleritis and endophthalmitis, as these infections require aggressive topical and systemic antibiotics as well as possible hospitalization.

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: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.153
Threshold uncertainty score0.875

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
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 head0.010
GPT teacher head0.283
Teacher spread0.273 · 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
Published2016
Admission routes1
Has abstractyes

Explore more

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