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Record W2519103599 · doi:10.12788/j.sder.2016.052

Vascular anomalies: differential diagnosis and mimickers

2016· review· en· W2519103599 on OpenAlexaff
Maria C. Garzón, Julie Powell

Bibliographic record

VenueSeminars in Cutaneous Medicine and Surgery · 2016
Typereview
Languageen
FieldMedicine
TopicVascular Malformations and Hemangiomas
Canadian institutionsUniversité de MontréalCentre Hospitalier Universitaire Sainte-Justine
Fundersnot available
KeywordsMedicineDifferential diagnosisHemangiomaVascular TumorsNatural historyRadiologyBroad spectrumVascular anomalyVascular malformationPathology

Abstract

fetched live from OpenAlex

Vascular anomalies are very common in children and encompass a wide spectrum of diseases. Many vascular anomalies can be mistaken for infantile hemangioma (IH). In addition, there is a variety of rare disorders including benign and malignant tumors that may mimic IH and other types of vascular anomalies. Understanding the clinical features, natural history, and typical clinical course of different types of vascular anomalies is essential in order to make the correct diagnosis and guide management. Radiologic imaging plays an important role in establishing the diagnosis; and when the diagnosis remains in doubt, a biopsy performed by a surgical specialist with expertise may prove to be lifesaving.

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)0.002
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 score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0040.002
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.001

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 head0.041
GPT teacher head0.310
Teacher spread0.268 · 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

Citations17
Published2016
Admission routes1
Has abstractyes

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