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Record W1986252942 · doi:10.3109/09546634.2014.975671

A retrospective chart review of the clinical efficacy of Nd:YAG 1064-nm laser for toenail onychomycosis

2014· article· en· W1986252942 on OpenAlexaff
Aditya Gupta, Maryse Paquet

Bibliographic record

VenueJournal of Dermatological Treatment · 2014
Typearticle
Languageen
FieldMedicine
TopicNail Diseases and Treatments
Canadian institutionsMediprobe Research (Canada)University of Toronto
Fundersnot available
KeywordsMedicineNail (fastener)Debridement (dental)DermatologyDermatophyteRetrospective cohort studySurgeryRegimenTerbinafineNail plateNail diseaseClinical efficacyLaser therapyDentistryAntifungalLaserComplicationItraconazole

Abstract

fetched live from OpenAlex

Cosmetic improvement in nail appearance is a great concern to patients with onychomycosis. Although oral and topical treatments for onychomycosis can potentially eradicate the infection, unsightly nails may remain despite negative mycology. Laser-based devices have been approved for the temporary clearance of nails with onychomycosis, thus providing a means of improving the aesthetic appearance of the nails. A retrospective chart review of patients treated with a Nd:YAG 1064-nm laser and debridement for onychomycosis, and terbinafine 1% cream for associated tinea pedis, between July 2012 and February 2014 was performed to ascertain the proportion of patients who achieved clinical outcomes. A temporary improvement in the appearance of the target nail was observed in 78% of patients and the affected area of the nail plate was reduced by at least 50% from baseline in 46% of patients. It appears that patients whose great toenails are potentially infected with non-dermatophyte molds may particularly benefit from laser therapy. Higher clinical outcome rates were observed with administration of four or more treatments, but additional observations and/or studies are needed to optimize the regimen of laser therapy to improve the cosmetic appearance of infected nails.

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 score0.019
Threshold uncertainty score0.366

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
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 head0.059
GPT teacher head0.383
Teacher spread0.325 · 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

Citations31
Published2014
Admission routes1
Has abstractyes

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