MétaCan
Menu
Back to cohort
Record W3012789772 · doi:10.1097/iop.0000000000001620

Upper Eyelid Isolated Arterio-Venous Malformation Treated With Embolization in a Patient With Keloid-Prone Skin

2020· article· en· W3012789772 on OpenAlexaff
Raffaella Capasso, Camilla Russo, Adriana Iuliano, Sirio Cocozza, Giuseppe Pontillo, Fabio Tortora, Diego Strianese, Andrea Elefante, Francesco Briganti

Bibliographic record

VenueOphthalmic Plastic and Reconstructive Surgery · 2020
Typearticle
Languageen
FieldMedicine
TopicVascular Malformations and Hemangiomas
Canadian institutionsTellabs (Canada)
Fundersnot available
KeywordsMedicineVenous malformationEyelidEmbolizationSurgeryDigital subtraction angiographyArteriovenous malformationRadiologyVascular malformationExternal carotid arteryForeheadAngiographyCarotid arteries

Abstract

fetched live from OpenAlex

Ocular adnexal aterio-venous malformations (AVMs) are rare congenital disabling anomalies, which may enlarge causing disfiguring deformity and rarely severe hemorrhage. These lesions are generally treated by preliminary endovascular embolization to shrink the arterio-venous malformation, followed by surgical gross total resection. The authors report a case of eyelid arterio-venous malformation in a 12-year-old girl, which progressively increased in size in few months. The patient complained mild itching, blurring of the vision, and mild tenderness. Magnetic resonance imaging showed an expansive mass with multiple arterial vessels at the left superior eyelid and left forehead. The diagnosis of arterio-venous malformation was then confirmed by digital subtraction angiography. Primary surgical excision was excluded because of the high risk of intrasurgical bleeding. Embolization through superselective cannulation of the left external carotid feeder vessels was performed resulting in flow exclusion up to the 80% of the nidus. Subsequent surgical resection was not recommended due to clinical evidence of keloid-prone skin.

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 score0.066
Threshold uncertainty score0.607

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.009
GPT teacher head0.194
Teacher spread0.185 · 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

Citations2
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueOphthalmic Plastic and Reconstructive SurgerySame topicVascular Malformations and HemangiomasFrench-language works237,207