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Record W1999799527 · doi:10.1155/2010/764843

The Sensitivity and Specificity of Potassium Hydroxide Smear and Fungal Culture Relative to Clinical Assessment in the Evaluation of Tinea Pedis: A Pooled Analysis

2010· article· en· W1999799527 on OpenAlexafffund
J. Levitt, Barrie Levitt, Arash Akhavan, Howard Yanofsky

Bibliographic record

VenueDermatology Research and Practice · 2010
Typearticle
Languageen
FieldMedicine
TopicNail Diseases and Treatments
Canadian institutionsMcGill University
FundersJewish General Hospital
KeywordsMedicineGold standard (test)DermatologyPotassium hydroxideAntifungalInternal medicineBiology

Abstract

fetched live from OpenAlex

Background. There are relatively few studies published examining the sensitivity and specificity of potassium hydroxide (KOH) smear and fungal culture examination of tinea pedis. Objective. To evaluate the sensitivity and specificity of KOH smear and fungal culture for diagnosing tinea pedis. Methods. A pooled analysis of data from five similarly conducted bioequivalence trials for antifungal drugs was performed. Data from 460 patients enrolled in the vehicle arms of these studies with clinical diagnosis of tinea pedis supported by positive fungal culture were analyzed 6 weeks after initiation of the study to determine the sensitivity and specificity of KOH smear and fungal culture. Results. Using clinical assessment as the gold standard, the sensitivities for KOH smear and culture were 73.3% (95% CI: 66.3 to 79.5%) and 41.7% (34.6 to 49.1%), respectively. The respective specificities for culture and KOH smear were 77.7% (72.2 to 82.5%) and 42.5% (36.6 to 48.6%). Conclusion. KOH smear and fungal culture are complementary diagnostic tests for tinea pedis, with the former being the more sensitive test of the two, and the latter being more specific.

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)0.086
metaresearch head score (Gemma)0.118
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.086
Threshold uncertainty score0.455

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0860.118
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0090.015
Bibliometrics0.0040.003
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.122
GPT teacher head0.523
Teacher spread0.401 · 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 designMeta-analysis
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

Citations66
Published2010
Admission routes2
Has abstractyes

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