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Record W3140975371 · doi:10.7573/dic.2020-12-6

Paediatrics: how to manage infantile haemangioma

2021· review· en· W3140975371 on OpenAlexaff
Jason Ho Seung Kim, Joseph M. Lam

Bibliographic record

VenueDrugs in Context · 2021
Typereview
Languageen
FieldMedicine
TopicVascular Malformations and Hemangiomas
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMedicinePediatricsIntensive care medicine

Abstract

fetched live from OpenAlex

Infantile haemangiomas (IHs) are relatively common benign vascular tumours found in the paediatric population. They have varying sizes and involve different depths in the skin leading to various colours, shapes and textures. Although considered harmless in most cases, they may lead to life-threatening complications or cause permanent disfigurations and organ dysfunction. For problematic IHs, the treatment options include oral and topical beta-blockers, systemic corticosteroids, laser treatment, and surgery. In this narrative review, the treatment options for problematic IH are compared and delivered concisely to facilitate the clinical decisions from practitioners, including those in primary care settings. Oral propranolol is currently considered the first-line intervention for problematic IHs. For superficial lesions, there is robust evidence for the use of topical timolol maleate. Systemic corticosteroids are sometimes used in specific situations such as resistance or contraindications to beta-blockers. Surgical excision can be considered in cases requiring urgent intervention such as airway obstruction; this can be done alongside laser therapies for the removal of residual tissue or when reconstructing areas of deformity. The combination of multiple treatment modalities may lead to a more rapid clinical response.

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 score0.007
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.003

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 head0.032
GPT teacher head0.321
Teacher spread0.289 · 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

Citations9
Published2021
Admission routes1
Has abstractyes

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