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Record W4400760568 · doi:10.1159/000539720

Intramuscular Hemangioma of Lateral Rectus Muscle with Rare Presentation as an Epibulbar Mass: A Case Report and Review of Literature

2024· article· en· W4400760568 on OpenAlexaff
Adwaita Nag, Hatem Krema, Zaid Saeed Kamil, Suzan Al-Mbaideen

Bibliographic record

VenueCase Reports in Oncology · 2024
Typearticle
Languageen
FieldMedicine
TopicVascular Malformations and Hemangiomas
Canadian institutionsUniversity of TorontoUniversity Health NetworkPrincess Margaret Cancer Centre
Fundersnot available
KeywordsMedicinePresentation (obstetrics)Extraocular musclesHemangiomaSurgical resectionOrbit (dynamics)ResectionAnatomySurgeryRadiology

Abstract

fetched live from OpenAlex

Introduction: Intramuscular hemangiomas of extraocular muscles are extremely rare tumors that usually present as retro-orbital masses causing proptosis. We describe a previously unreported presentation, in the form of an epibulbar mass; this easily accessible location allows direct imaging, complete surgical resection, and histopathological confirmation, providing a unique perspective. Case Presentation: A 69-year-old woman presented with a painless dark red mass in the lateral part of the right eye, which had been slowly enlarging over the last 18 months. Clinical features and imaging were suggestive of a benign vascular tumor of the conjunctiva. During surgical resection, the mass was observed to be enmeshed within the fibers of the lateral rectus muscle. Careful dissection from muscle fibers was needed for complete excision. Histopathology revealed the diagnosis of an intramuscular hemangioma of extraocular muscle. Conclusion: In this report, we describe the atypical anterior epibulbar presentation of intramuscular hemangioma of the lateral rectus muscle. We discuss the differential diagnoses and management of this rare tumor along with a review of existing literature. Careful surgical resection achieved complete resolution in this case without recurrence.

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 score0.038
Threshold uncertainty score0.429

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
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 head0.012
GPT teacher head0.331
Teacher spread0.319 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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