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Record W2736187237 · doi:10.12968/npre.2017.15.7.324

Dermatology prescribing update: Eczema

2017· article· en· W2736187237 on OpenAlexaff
Julie Van Onselen

Bibliographic record

VenueNurse Prescribing · 2017
Typearticle
Languageen
FieldMedicine
TopicDermatology and Skin Diseases
Canadian institutionsLearning Partnership
Fundersnot available
KeywordsMedicineAtopic dermatitisDermatologyHand eczemaPrimary careIntensive care medicineAllergyContact dermatitisFamily medicineImmunology

Abstract

fetched live from OpenAlex

Eczema is a common skin condition, which for the majority of suffers is managed in primary care. Nurse prescribers should be aware of evidence-based guidelines in eczema treatment, on which they need to base prescribing decisions and work with the patient on individual skin-care plans. Education and support in managing eczema is essential for patients of all ages (and carers of children and older people). Eczema has a huge impact on quality of life, but a good management plan can make a big difference to controlling this chronic condition. This article explains the principles of eczema treatments in acute, sub-acute and chronic stages of eczema with emollients, topical corticosteroids, antibiotics, antimicrobials and antivirals, topical calcineurin inhibitors and antihistamines.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.164
Threshold uncertainty score0.904

Codex and Gemma teacher scores by category

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

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 head0.031
GPT teacher head0.309
Teacher spread0.278 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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
Published2017
Admission routes1
Has abstractyes

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