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Record W4392587390 · doi:10.1111/jdv.19937

Consensus statement on the diagnosis and treatment of sclerosing diseases of the skin, Part 2: Scleromyxoedema and scleroedema

2024· article· en· W4392587390 on OpenAlexfundno aff
Robert Knobler, Marija Geroldinger‐Simić, Alexander Kreuter, Nicolas Hunzelmann, Pia Moinzadeh, Franco Rongioletti, Christopher P. Denton, Luc Mouthon, Maurizio Cutolo, Vanessa Smith, Armando Gabrielli, M. Bagot, Anne Braae Olesen, Ivan Foeldvari, Ahmad Jalili, Veli‐Matti Kähäri, Sarolta Kárpáti, Małgorzata Olszewska, Jaana Panelius, Pietro Quaglino, Julien Sénéschal, Michael Sticherling, Cord Sunderkötter, Adrian Tanew, Peter Wolf, Margitta Worm, Anna Skrok, Lidia Rudnicka, Thomas Krieg

Bibliographic record

VenueJournal of the European Academy of Dermatology and Venereology · 2024
Typearticle
Languageen
FieldMedicine
TopicSkin Diseases and Diabetes
Canadian institutionsnot available
FundersMallinckrodt Pharmaceuticals
KeywordsMedicineStatement (logic)DermatologyIntensive care medicineEpistemology

Abstract

fetched live from OpenAlex

The term 'sclerosing diseases of the skin' comprises specific dermatological entities, which have fibrotic changes of the skin in common. These diseases mostly manifest in different clinical subtypes according to cutaneous and extracutaneous involvement and can sometimes be difficult to distinguish from each other. The present consensus provides an update to the 2017 European Dermatology Forum Guidelines, focusing on characteristic clinical and histopathological features, diagnostic scores and the serum autoantibodies most useful for differential diagnosis. In addition, updated strategies for the first- and advanced-line therapy of sclerosing skin diseases are addressed in detail. Part 2 of this consensus provides clinicians with an overview of the diagnosis and treatment of scleromyxoedema and scleroedema (of Buschke).

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)0.019
metaresearch head score (Gemma)0.036
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: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.019
Threshold uncertainty score0.100

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0190.036
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.005
Bibliometrics0.0040.002
Science and technology studies0.0020.002
Scholarly communication0.0020.002
Open science0.0060.003
Research integrity0.0100.011
Insufficient payload (model declined to judge)0.0050.005

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 head0.048
GPT teacher head0.292
Teacher spread0.243 · 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
GenreOther

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

Citations12
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueJournal of the European Academy of Dermatology and VenereologySame topicSkin Diseases and DiabetesFrench-language works237,207