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Record W1963596957 · doi:10.1055/s-0029-1237692

Complex Combined Vascular Malformations and Vascular Malformation Syndromes Affecting the Extremities in Children

2009· review· en· W1963596957 on OpenAlexaff
Edrise M. Lobo-Mueller, João Amaral, Paul Babyn, Qiuyan Wang, Philip John

Bibliographic record

VenueSeminars in Musculoskeletal Radiology · 2009
Typereview
Languageen
FieldMedicine
TopicVascular Malformations and Hemangiomas
Canadian institutionsHospital for Sick Children
Fundersnot available
KeywordsMedicineKlippel-Trenaunay syndromeVascular malformationVascular diseaseMagnetic resonance imagingVascular anomalyVenous malformationVascular networkVascular TumorsHemihypertrophyRadiologyPathologyHemangiomaSoft tissueAnatomySurgery

Abstract

fetched live from OpenAlex

Complex combined vascular malformations affecting extremities are an interesting group of vascular malformations because, in addition to the vascular channel anomalies present, they can be associated with other tissue changes and sometimes altered limb growth. At present, magnetic resonance imaging is the gold standard imaging tool to evaluate such complex conditions in children because of its inherent tissue specificity and vascular capabilities that enables characterization of tissues and the vascular channel anomalies both for diagnosis and management of the patient. A brief review of some of these conditions is presented, including Klippel-Trénaunay syndrome, Parkes Weber syndrome, extensive diffuse low-flow venous malformations, Bannayan-Riley-Ruvalcaba syndrome, cutis marmorata telangiectatica congenita, Maffucci's syndrome, and Gorham-Stout disease. KEYWORDS Complex vascular anomalies - Gorham-Stout disease - Klippel-Trénaunay syndrome - Maffucci's syndrome - Parkes Weber syndrome - Proteus syndrome - magnetic resonance imaging - vascular malformations

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.003
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.002

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 head0.022
GPT teacher head0.307
Teacher spread0.285 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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

Citations40
Published2009
Admission routes1
Has abstractyes

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