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Record W4396684814 · doi:10.15690/vramn9965

Controversial Issues of Immunopathogenesis of Psoriasis and Atopic Dermatitis

2024· article· en· W4396684814 on OpenAlexaff
Eduard Т. Ambarchian, Leyla S. Namazova-Baranova, Anastasia D. Kuzminova, Vladislav V. Ivanchikov, Еlena A. Vishneva, Marika I. Ivardava, Kamilla E. Efendiyeva, Juliya G. Levina

Bibliographic record

VenueAnnals of the Russian academy of medical sciences · 2024
Typearticle
Languageen
FieldMedicine
TopicDermatology and Skin Diseases
Canadian institutionsChildren’s Health Research Institute
Fundersnot available
KeywordsPsoriasisAtopic dermatitisItchingMedicinePathogenesisImmunologyRashDermatologyDiseaseImmunoglobulin EDominance (genetics)AntibodyPathologyBiology

Abstract

fetched live from OpenAlex

Psoriasis (PsO) and atopic dermatitis (AD have much in common: both diseases are widespread, characterized by a chronic relapsing course, primarily affect the skin and lead to a quality reduction of life of patients, regardless of their age. The pathogenesis of these two dermatoses, which are the most common in the practice of a pediatric dermatologist, is quite different. PsO is a chronic inflammatory skin disease, the pathogenesis of which is associated with the involvement of the Th1 pathway: Th17 cells and the IL-23/IL-17 axis. AD, in turn, is usually associated with high levels of IL-4, IL-5, IL-13, IL-31 and IFN-γ produced by activated T-helper 2 (Th2) cells. The clinical symptoms and immunopathological responses of these two skin conditions tend to differ. However, patients with PsO may sometimes present with a skin rash resembling AD combined with intense itching and laboratory increase in immunoglobulin E (IgE) which may indicate the need to change the paradigm of dominance of only one type of T-inflammation in patients with these diseases.

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)0.008
metaresearch head score (Gemma)0.011
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 score0.008
Threshold uncertainty score0.040

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.013
Scholarly communication0.0030.005
Open science0.0020.002
Research integrity0.0060.008
Insufficient payload (model declined to judge)0.0030.001

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 head0.052
GPT teacher head0.376
Teacher spread0.324 · 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

Citations1
Published2024
Admission routes1
Has abstractyes

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Same venueAnnals of the Russian academy of medical sciencesSame topicDermatology and Skin DiseasesFrench-language works237,207