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Record W4404489452 · doi:10.1155/dth/9678447

Pharmaceutical Management of Rosacea—An Australian/New Zealand Medical Dermatology Consensus Narrative

2024· article· en· W4404489452 on OpenAlexfundno aff
Marius Rademaker, Peter Foley, John Sullivan, Kathy Armour, Christopher Baker, A Ferguson, Kurt Gebauer, Monisha Gupta, Gillian Marshman, Erin McMeniman, Diana Rubel, Li‐Chuen Wong

Bibliographic record

VenueDermatologic Therapy · 2024
Typearticle
Languageen
FieldMedicine
TopicAcne and Rosacea Treatments and Effects
Canadian institutionsnot available
FundersCilagIncyteDermiraGenentechValeant Pharmaceuticals InternationalArgenxCelgeneBiogenSun PharmaRegeneron PharmaceuticalsTeva Pharmaceutical IndustriesGaldermaAstraZenecaEli Lilly and CompanyAmgen
KeywordsMedicineRosaceaDermatologyConsensus conferenceMEDLINEFamily medicineAcneInternal medicine

Abstract

fetched live from OpenAlex

Rosacea, a common chronic, predominantly centro‐facial dermatosis, has previously been classified into distinct subtypes with a range of morphological signs that overlap with other inflammatory skin disorders. Recently, there has been a move towards diagnosis of clinical phenotypes, largely driven by a better understanding of the pathophysiology of rosacea and clinical trial endpoints. Despite this, treatment remains a challenge. The Australasian Medical Dermatology Group held a Rosacea workshop in November 2022 to develop a practical narrative. Eighteen recommendations were agreed upon using a modified eDelphi process in the first round, including a rosacea treatment algorithm.

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)0.032
metaresearch head score (Gemma)0.048
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: Empirical · Consensus signal: none
Teacher disagreement score0.032
Threshold uncertainty score0.172

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0320.048
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0030.003
Scholarly communication0.0050.006
Open science0.0030.007
Research integrity0.0100.014
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 head0.047
GPT teacher head0.383
Teacher spread0.336 · 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
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

Citations2
Published2024
Admission routes1
Has abstractyes

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