The Role of Shoe and Sock Sanitization in the Management of Superficial Fungal Infections of the Feet
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".