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Record W2282704344 · doi:10.1517/14656566.2016.1146687

How effective is efinaconazole in the management of onychomycosis?

2016· article· en· W2282704344 on OpenAlexaffabout
Aditya Gupta, Maria Cernea

Bibliographic record

VenueExpert Opinion on Pharmacotherapy · 2016
Typearticle
Languageen
FieldMedicine
TopicNail Diseases and Treatments
Canadian institutionsUniversity of TorontoMediprobe Research (Canada)
Fundersnot available
KeywordsMedicineDermatologyAdverse effectBroad spectrumAntifungalCure rateCombination therapySurgeryInternal medicine

Abstract

fetched live from OpenAlex

INTRODUCTION: Onychomycosis is a fungal nail infection that is difficult to treat due to poor accessibility of drugs into the nail plate. Although oral antifungals can reach the nail apparatus more readily, these therapies may not be suitable or desirable for some patients (e.g., multiple medications or immunocompromised). Efinaconazole 10% solution is a new topical antifungal recently approved and sold in Canada, the United States and Japan for the treatment of mild-to-moderate toenail onychomycosis. Efinaconazole has broad-spectrum antifungal activity against dermatophytes, nondermatophyte molds and yeasts, and high ungual penetration due to its low keratin binding properties. AREAS COVERED: The objective of this article is to summarize recent data regarding the efficacy, safety and pharmacokinetic properties of efinaconazole in the treatment of onychomycosis. EXPERT OPINION: Efinaconazole is a safe and effective treatment for onychomycosis that can be used in a wide range of patients due to its broad-spectrum antifungal activity and low rate of treatment-related adverse events. When incomplete response to oral therapy or devices (e.g. laser therapy) is encountered, efinaconazole could be used in combination to improve success rates. Alternatively, efinaconazole could be used as a 'closer' drug, in an effort to provide cure when the initial oral or device therapy has resulted in an incomplete response.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.923
Threshold uncertainty score0.301

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.026
GPT teacher head0.368
Teacher spread0.341 · 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 designOther design
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

Citations17
Published2016
Admission routes2
Has abstractyes

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