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Record W3013237965 · doi:10.1097/der.0000000000000589

<i>Staphylococcus aureus</i> in Atopic Dermatitis: Past, Present, and Future

2020· review· en· W3013237965 on OpenAlexvenueno aff
Jonwei Hwang, Joanna Jaros, Vivian Y. Shi

Bibliographic record

VenueDermatitis · 2020
Typereview
Languageen
FieldMedicine
TopicDermatology and Skin Diseases
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineAtopic dermatitisStaphylococcus aureusDermatologyBacteria

Abstract

fetched live from OpenAlex

: The role of Staphylococcus aureus (SA) in the pathogenesis and management in atopic dermatitis is rapidly evolving. The modern understanding of SA in atopic dermatitis now includes an expanded array of virulence factors, the interplay of clonal and temporal shifts in SA populations, and host factors such as filaggrin and natural moisturizing factor. New, emerging therapies that focus on long-term, targeted elimination of SA colonization are currently under investigation (Br J Dermatol 2017;17(1)63-71). Herein, we discuss and review the latest staphylococcal and microbiome-modifying therapies including topical antibiotics, topical natural oil fatty acids, anti-SA vaccines, microbial transplantation, vitamin D supplementation, dupilumab and proposed future investigative directions.

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 score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.002

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 head0.017
GPT teacher head0.287
Teacher spread0.271 · 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

Citations17
Published2020
Admission routes1
Has abstractyes

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