MétaCan
Menu
Back to cohort
Record W4367301164 · doi:10.14740/gr1603

Esophageal Ulcer After Intravitreal Ranibizumab Injection in a Patient With Age-Related Macular Degeneration

2023· article· en· W4367301164 on OpenAlexvenueno aff
Xin Qing Li, Ke‐Wei Zhu, Jun Lai, Jian Wu, Xiao Guo

Bibliographic record

VenueGastroenterology Research · 2023
Typearticle
Languageen
FieldMedicine
TopicRetinal Diseases and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineMacular degenerationRanibizumabOphthalmologySurgeryBevacizumabChemotherapy

Abstract

fetched live from OpenAlex

Ranibizumab is a monoclonal antibody fragment targeted against vascular endothelial growth factor (VEGF) A isoform (VEGF-A). This study aimed to report a case of esophageal ulcer that developed soon after intravitreal ranibizumab injection in a patient with age-related macular degeneration (AMD). A 53-year-old male patient diagnosed with AMD received ranibizumab through intravitreal injection in the left eye. Mild dysphagia occurred 3 days after receiving intravitreal ranibizumab injection for the second time. The dysphagia exacerbated remarkably and was accompanied by hemoptysis 1 day after receiving ranibizumab for the third time. Severe dysphagia accompanied by intense retrosternal pain and pant emerged after injecting ranibizumab for the fourth time. An esophageal ulcer was observed through ultrasound gastroscopy, covered with fibrinous tissue, and surrounded by flushing and congestive mucosae. The patient received proton pump inhibitor (PPI) therapy combined with traditional Chinese medicine (TCM) after discontinuation of ranibizumab. The dysphagia and retrosternal pain were gradually relieved after treatment. Afterwards, the esophageal ulcer has not relapsed since permanent discontinuation of ranibizumab. To our best knowledge, this was the first case of esophageal ulcer related to intravitreal ranibizumab injection. Our study indicated that VEGF-A played a potential role in the development of esophageal ulceration.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.022
Threshold uncertainty score0.411

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.021
GPT teacher head0.324
Teacher spread0.303 · 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 designObservational
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

Citations1
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueGastroenterology ResearchSame topicRetinal Diseases and TreatmentsFrench-language works237,207