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Record W3087416500 · doi:10.1097/der.0000000000000634

Pearls for Managing Atopic Dermatitis in Patients With Low Socioeconomic Status

2020· article· en· W3087416500 on OpenAlexaffvenue
Muskaan Sachdeva, Kyla N. Price, Jennifer L. Hsiao, Vivian Y. Shi

Bibliographic record

VenueDermatitis · 2020
Typearticle
Languageen
FieldMedicine
TopicDermatology and Skin Diseases
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMedicineSocioeconomic statusAtopic dermatitisDermatologyEnvironmental health

Abstract

fetched live from OpenAlex

Atopic dermatitis (AD) is a chronic inflammatory dermatosis presenting with inflamed and itchy skin. Recent studies have shown an inverse relationship between socioeconomic status and the severity of AD. Low socioeconomic status (LSES) individuals with AD face specific barriers that may impede management. These include forgoing doctor's appointments due to transportation costs, inability to take time off from work, and lack of affordable childcare services. Unaffordable medications and over-the-counter products for managing AD further present as significant challenges for LSES patients. This article aims to offer practical and affordable recommendations to help mitigate the challenges faced by LSES patients with AD and thereby alleviate disease burden and improve treatment outcomes.

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)0.001
metaresearch head score (Gemma)0.003
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: Commentary · Consensus signal: none
Teacher disagreement score0.012
Threshold uncertainty score0.041

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0120.002

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 head0.007
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
GenreCommentary

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

Citations3
Published2020
Admission routes2
Has abstractyes

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