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Record W2802087739 · doi:10.1177/1203475418770570

Medical, Surgical, and Wound Care Management of Ulcerated Infantile Hemangiomas: A Systematic Review

2018· review· en· W2802087739 on OpenAlexaff
Jane Y. Wang, Arvin Ighani, Ana Patricia Ayala, Sadanori Akita, Irene Lara‐Corrales, Afsáneh Alavi

Bibliographic record

VenueJournal of Cutaneous Medicine and Surgery · 2018
Typereview
Languageen
FieldMedicine
TopicVascular Malformations and Hemangiomas
Canadian institutionsSickKids FoundationHospital for Sick ChildrenWomen's College HospitalUniversity of Toronto
Fundersnot available
KeywordsMedicineCINAHLMEDLINEObservational studyRandomized controlled trialSurgeryDermatologySystematic reviewInternal medicinePsychological intervention

Abstract

fetched live from OpenAlex

Ulcerated infantile hemangiomas may present a therapeutic challenge, especially if there is concurrent hemorrhage or infection. The aim of this study was to systematically review the published evidence on the treatment of ulcerated hemangiomas, focusing on wound healing as the outcome of interest. We searched MEDLINE, Embase, SCOPUS, Cumulative Index to Nursing and Allied Health Literature (CINAHL), and Web of Science from inception to July 2016. Seventy-seven studies met our inclusion criteria. One study was a randomized controlled trial, 30 were observational studies, and 46 were case reports or case series. There is significant heterogeneity among the methods used. We reviewed 1239 patients in total. Of the 197 treated with oral propranolol, 191 (97.0%) achieved complete ulcer healing. Thirty-one patients failed corticosteroid therapy (oral, intralesional, or topical) and were subsequently successfully treated with other therapies. Surgical resections were typically performed for larger hemangiomas and those causing complications. None of the therapies discussed appear to offer significant advantages over others. Therefore, treatment decisions should be individualized based on location of disease, extent, symptoms, feasibility, cost, and parental preference.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.404
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0070.001
Bibliometrics0.0010.001
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 head0.030
GPT teacher head0.330
Teacher spread0.300 · 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.

Study designSystematic review
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

Citations25
Published2018
Admission routes1
Has abstractyes

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