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Record W4226108934 · doi:10.12968/joan.2022.11.3.110

An introduction to treating rosacea

2022· article· en· W4226108934 on OpenAlexaff
Paul Charlson

Bibliographic record

VenueJournal of Aesthetic Nursing · 2022
Typearticle
Languageen
FieldMedicine
TopicAcne and Rosacea Treatments and Effects
Canadian institutionsSKiN Health
Fundersnot available
KeywordsRosaceaMedicineDermatologyMetronidazoleAzelaic acidErythemaMelasmaClarithromycinIvermectinAntibioticsAcneHelicobacter pyloriInternal medicineMicrobiologyVeterinary medicine

Abstract

fetched live from OpenAlex

Rosacea is a fairly common condition, especially in fair-haired, blue-eyed people, that mainly affects the centre of the face, causing erythema and papules and pustules. It is a chronic condition and requires correct diagnosis and careful management to achieve optimal results. Gentle skincare is vital. Topical agents, such as azelaic acid, metronidazole, ivermectin and brimodinine tartrate are useful. Oral antibiotics, such as doxcycline and clarithromycin, as well as isotretinion, also play a role. Aesthetic clinics may see patients presenting for laser or intense pulsed light treatments for the telangectasia that is often present. This forms only part of the overall package of treatments.

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: Not applicable
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.120
Threshold uncertainty score0.400

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.002
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.1200.050

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 head0.008
GPT teacher head0.303
Teacher spread0.295 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

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