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Record W2102189434 · doi:10.4103/2152-7806.129560

Surgical management of large scalp infantile hemangiomas

2014· article· en· W2102189434 on OpenAlexaff
RobertJ Singer, Erin N. Kiehna, KomalF Satti, Moneeb Ehtesham, Mahan Ghiassi

Bibliographic record

VenueSurgical Neurology International · 2014
Typearticle
Languageen
FieldMedicine
TopicVascular Malformations and Hemangiomas
Canadian institutionsHospital for Sick Children
Fundersnot available
KeywordsMedicineScalpDeformitySurgerySurgical resectionSurgical excisionHemangioma

Abstract

fetched live from OpenAlex

BACKGROUND: Infantile Hemangiomas (IH) are the most common benign tumor of infancy, occurring in over 10% of newborns. While most IHs involute and never require intervention, some scalp IHs may cause severe cosmetic deformity and threaten tissue integrity that requires surgical excision. CASE DESCRIPTION: We present our experience with two infants who presented with large scalp IH. After vascular imaging, the patients underwent surgical resection of the IH and primary wound closure with excellent cosmetic outcome. We detail the surgical management of these cases and review the relevant literature. CONCLUSION: In some cases the IHs leave behind fibro-fatty residuum causing contour deformity. Surgery is often required for very large lesions causing extensive anatomical and/or functional disruption. The goal of surgical intervention is to restore normal anatomic contour and shape while minimizing the size of the permanent scar.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.857
Threshold uncertainty score0.999

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)0.0020.000

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 head0.007
GPT teacher head0.277
Teacher spread0.270 · 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 designNot applicable
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

Citations6
Published2014
Admission routes1
Has abstractyes

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