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Record W4308133045 · doi:10.15690/vsp.v21i5.2449

Innovations in Therapeutic Improvement of the Cutaneous Microbiome in Children with Atopic Dermatitis

2022· article· en· W4308133045 on OpenAlexaff
Nikolay N. Murashkin, Roman V. Epishev, Roman A. Ivanov, Alexander I. Materikin, Leonid A. Opryatin, Alena A. Savelova, Roza Y. Nezhvedilova, Eduard Т. Ambarchian, Dmitri V. Fedorov, Lyudmila L. Rusakova

Bibliographic record

VenueВопросы современной педиатрии · 2022
Typearticle
Languageen
FieldMedicine
TopicDermatology and Skin Diseases
Canadian institutionsChildren’s Health Research Institute
Fundersnot available
KeywordsAtopic dermatitisMicrobiomeBiofilmStaphylococcus aureusExacerbationImmunologyDermatologySkin infectionMicrobiologyMedicineBiologyBacteriaBioinformaticsGenetics

Abstract

fetched live from OpenAlex

Biofilm is the dominant form of skin microbiota organization that provides adhesion and preservation of microorganisms in the skin micro-environment. It is necessary to ensure epidermal barrier function and local immunomodulation. Staphylococcus aureus becomes the major colonizer of skin lesions in case of atopic dermatitis exacerbation, and it also can form the biofilms. S. aureus growth and biofilm formation due to other microbial commensals on the skin of patients with atopic dermatitis leads to chronic output of pro-inflammatory cytokines and later to abnormalities in healthy skin microbiome. The role of microbial biofilm in human’s health makes the skin microbiota an attractive target for therapeutic intervention in various skin 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)0.000
metaresearch head score (Gemma)0.001
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 score0.004
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.006
GPT teacher head0.225
Teacher spread0.219 · 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

Citations4
Published2022
Admission routes1
Has abstractyes

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