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Record W2013790629 · doi:10.1159/000371880

Eruptive Disseminated Pyogenic Granulomas following Lightning Injury

2015· article· en· W2013790629 on OpenAlexafffund
Elena Netchiporouk, Linda Moreau, Lucie P. Ramirez, Patricia A.C. Castillo, Francisco Bravo, Manuel C. Del Solar, Denis Sasseville, César Ramos-Remus

Bibliographic record

VenueDermatology · 2015
Typearticle
Languageen
FieldMedicine
TopicVascular Malformations and Hemangiomas
Canadian institutionsMcGill University Health Centre
FundersCanadian Dermatology FoundationDermatology Foundation
KeywordsMedicineLesionNodule (geology)BiopsyPyogenic granulomaTelangiectasiaPathologySurgeryDermatologyBiology

Abstract

fetched live from OpenAlex

BACKGROUND: Pyogenic granuloma (PG) is a common benign acquired vascular tumor. It classically presents as a solitary friable nodule on the face or distal extremities. Disseminated eruption is rare and can occur spontaneously or secondary to various triggers, including burn injury. To date, the literature reports only 13 cases of eruptive PGs following burn injury, most from exposure to boiling milk or water. We report the first case of disseminated eruptive PGs following a lightning injury. CASE: A 17-year-old previously healthy boy developed second- and third-degree burns following lightning injury. Two weeks later, he developed widespread dark-purple polypoid exophytic tumors ranging from 1 to 10 cm in diameter extending beyond the limits of the initial burn injury. The lesions were friable and often formed erosions and crusts. The patient was otherwise well and laboratory and microbiological investigations were normal. Excisional biopsy of a lesion was diagnostic of PG and the patient was treated with surgical excision of the lesions, without recurrence. CONCLUSION: The exact pathogenesis of multiple PGs remains unknown. Several pathogenic mechanisms have been suggested, including production of angiogenic factors that stimulate endothelial proliferation and formation of minute arteriovenous fistulas by trauma.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.389
Threshold uncertainty score0.454

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 head0.024
GPT teacher head0.308
Teacher spread0.284 · 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 designObservational
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

Citations10
Published2015
Admission routes2
Has abstractyes

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