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Record W4236755953 · doi:10.17140/pnnoj-1-102

Infantile Hemangioma

2014· article· en· W4236755953 on OpenAlexaff
Alexander K. C. Leung, Benjamin Barankin, Kam‐Lun Ellis Hon

Bibliographic record

VenuePediatrics and Neonatal Nursing - Open Journal · 2014
Typearticle
Languageen
FieldMedicine
TopicVascular Malformations and Hemangiomas
Canadian institutionsUniversity of CalgaryAlberta Children's Hospital
Fundersnot available
KeywordsHemangiomaMedicineRadiology

Abstract

fetched live from OpenAlex

Infantile hemangiomas typically appear in the first few weeks of life as areas of pallor, followed by telangiectatic patches. They then grow rapidly in the first 3 to 6 months of life. Superficial lesions are bright red, protuberant, and sharply demarcated and are often referred to as "strawberry hemangiomas". Deep lesions are bluish and dome-shaped, feel like a "bag of worms", and are compressible. Infantile hemangiomas have a predilection for the head and neck region. Most infantile hemangiomas exist as solitary lesions. Infantile hemangiomas continue to grow until 9 to 12 months of age, at which time the growth rate slows down to parallel the growth of the child. Half of these lesions will show complete involution by the time a child reaches age 5; 70% will have disappeared by age 7; and 95% will have regressed by ages 10 to 12. The majority of infantile hemangiomas require no treatment. Indications for active intervention include severe or recurrent hemorrhage unresponsive to treatment, threatening ulceration in areas where serious complications might ensue, interference with vital structures, pedunculated hemangiomas, and significant disfigurement. Treatment options include systemic corticosteroids, intralesional corticosteroids, topical and oral beta blockers, pulsed-dye laser, and less commonly interferon- or surgical resection. In recent years, propranolol, a nonselective -blocker, has been preferentially used as a first-line treatment of problematic infantile hemangioma.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.923
Threshold uncertainty score0.368

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 head0.013
GPT teacher head0.288
Teacher spread0.275 · 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 designOther design
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

Citations5
Published2014
Admission routes1
Has abstractyes

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Same venuePediatrics and Neonatal Nursing - Open JournalSame topicVascular Malformations and HemangiomasFrench-language works237,207