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Onychomycosis: A 3‐Year Clinicomycologic Hospital‐Based Study

2007· article· en· W2089912746 on OpenAlexaff
Virendra Sehgal, Ashok Aggarwal, Govind Srivastava, Manish Gupta, Anuradha Chaudhary

Bibliographic record

VenueSKINmed Dermatology for the Clinician · 2007
Typearticle
Languageen
FieldMedicine
TopicNail Diseases and Treatments
Canadian institutionsSKiN Health
Fundersnot available
KeywordsMedicineTrichophyton rubrumDermatologyTrichophytonEpidemiologyPathologyAntifungal

Abstract

fetched live from OpenAlex

BACKGROUND: Despite onychomycosis being an established entity, only a few studies are available from the Indian subcontinent. The authors investigated the comprehensive pattern of the condition. AIM: To investigate the epidemiologic, clinical, and mycologic factors associated with onychomycosis in 50 patients using a prospective study design. METHODS: Fifty patients with potassium hydroxide-positive tests were evaluated according to a predetermined protocal recording details of epidemiologic, clinical, and mycologic characteristics. The collected data were analyzed to determine the correlation of various parameters. RESULTS: Distal and lateral subungual onychomycosis, total dystrophic onychomycosis, and superficial white onychomycosis variants of onychomycosis were identified, mostly in men 21-30 years of age (mean age, 34.5 years). Epidemiologic characteristics were instrumental to either initiate, perpetuate, or disseminate the disease process. Trichophyton rubrum and Trichophyton mentagrophytes were the main causative dermatophytes; yeasts and molds were less common. CONCLUSIONS: Recognition of onychomycosis is less difficult providing the clinician is aware of the entity. Should the etiologic diagnosis be made, its eradication is desirable to surmount its implication in the society at large.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.008
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0020.001
Scholarly communication0.0010.002
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.033
GPT teacher head0.375
Teacher spread0.342 · 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 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

Citations5
Published2007
Admission routes1
Has abstractyes

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