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Record W3026002900 · doi:10.1177/1203475420923644

Common Atopic Dermatitis Rating Scales: A Practical Approach and Brief Review

2020· review· en· W3026002900 on OpenAlexaff
Yue Yang, Charles Lynde, Patrick Fleming

Bibliographic record

VenueJournal of Cutaneous Medicine and Surgery · 2020
Typereview
Languageen
FieldMedicine
TopicDermatology and Skin Diseases
Canadian institutionsLynde Centre for DermatologyUniversity of TorontoUniversity of British Columbia
Fundersnot available
KeywordsAtopic dermatitisMedicineRating scaleGovernment (linguistics)DermatologyPsychologyDevelopmental psychology

Abstract

fetched live from OpenAlex

Atopic dermatitis (AD) severity measurement scales are important in clinical trials as objective outcome measures and are often required for government and private insurance plans. These scales are sometimes underused by clinicians due to a variety of factors including time constraints and lack familiarity. We conducted a literature review on the most commonly used AD measurement scales and provide succinct user guides and scoring explanations, advantages and disadvantages, and interscale comparisons.

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)0.004
metaresearch head score (Gemma)0.009
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 score0.010
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.009
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0030.003
Bibliometrics0.0100.007
Science and technology studies0.0000.001
Scholarly communication0.0020.003
Open science0.0020.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0070.003

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 head0.066
GPT teacher head0.362
Teacher spread0.296 · 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

Citations13
Published2020
Admission routes1
Has abstractyes

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