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Record W2020659835 · doi:10.1097/mop.0b013e328362c3f6

New therapies under development for psoriasis treatment

2013· review· en· W2020659835 on OpenAlexaff
Martha‐Estrella García‐Pérez, Tatjana Stevanovic, Patrice E. Poubelle

Bibliographic record

VenueCurrent Opinion in Pediatrics · 2013
Typereview
Languageen
FieldImmunology and Microbiology
TopicPsoriasis: Treatment and Pathogenesis
Canadian institutionsUniversité LavalCentre de Géomatique du Québec
Fundersnot available
KeywordsMedicinePsoriasisJanus kinaseClinical trialAdverse effectMechanism (biology)ApremilastIntensive care medicineBioinformaticsPharmacologyDermatologyImmunologyCytokineInternal medicinePsoriatic arthritis

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: An improved understanding of the psoriasis pathogenesis has provided new insights into potential new therapeutic targets, which has positively influenced the development of novel therapies. This monograph reviews recent clinical trials concerning new small molecules and biotech products under investigation for plaque psoriasis treatment. Emphasis is placed on mechanism of action, efficacy and adverse effects of these new agents. RECENT FINDINGS: Recent literature has shown that there are several new drugs under development for psoriasis treatment including new A3 adenosine receptor agonists, biologics like anti-tumor necrosis factor, anti-interleukin-17, anti-interleukin-12/23 and anti-interleukin-17 receptor agents, as well as Janus kinase and phosphodiesterase 4 inhibitors, among others. Although clinical trials were too short for predicting the real long-term safety of these treatments, other studies longer than those presently available are expected in the future. SUMMARY: On the basis of novel advances in psoriasis therapy, treatment paradigms could change in the following years. However, the real contribution of these new drugs to the antipsoriatic therapeutic armamentarium still needs to be established.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.976
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.146
GPT teacher head0.368
Teacher spread0.222 · 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.

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

Citations16
Published2013
Admission routes1
Has abstractyes

Explore more

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