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Record W2790583845 · doi:10.7547/17-043

The Role of Shoe and Sock Sanitization in the Management of Superficial Fungal Infections of the Feet

2018· review· en· W2790583845 on OpenAlexaff
Aditya Gupta, Sarah G. Versteeg

Bibliographic record

VenueJournal of the American Podiatric Medical Association · 2018
Typereview
Languageen
FieldMedicine
TopicNail Diseases and Treatments
Canadian institutionsMediprobe Research (Canada)University of Toronto
Fundersnot available
KeywordsMedicineDermatologyIntensive care medicine

Abstract

fetched live from OpenAlex

Because of the ubiquitous nature of dermatophytes and a lack of an adaptive immune response in the nail plate, recurrence and relapse rates associated with superficial fungal infections are high (10%-53%). Cured or improved dermatophytosis patients could become reinfected if exposed to fungal reservoirs, such as an infected shoe, sock, or textile. To prevent this, footwear, sock, and textile sanitization methods can be used. To provide insight into effective sanitization options, the focus of this article is to review footwear, sock, and textile sanitization studies conducted throughout history (1920-2016). Thirty-three studies are covered in this review, encompassing techniques ranging from formaldehyde fumigation and foot powder application, to more modern approaches such as UV light and silver-light irradiation technologies. Older sanitization methods (eg, boiling, use of chlorine and salts) are quite limited in their practicality, as they can result in health complications and ruin shoe integrity. Newer approaches to shoe and sock sanitization, such as ozone application and UV irradiation, have shown very promising results. Further research is still needed with these modern techniques, as knowledge gaps and cost prevent the creation of standardized parameters for successful use. By combining sanitization methods with other preventative measures, protection against reinfection may be enhanced.

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)0.002
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: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.851
Threshold uncertainty score0.235

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
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 head0.009
GPT teacher head0.301
Teacher spread0.292 · 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
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

Citations23
Published2018
Admission routes1
Has abstractyes

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