An update on tinea capitis in children
Bibliographic record
Abstract
Tinea capitis presents a significant public health care challenge due to its contagious nature, and potential long-term consequences if unrecognized and untreated. This review explores the prevalence, risk factors, diagnostic methods, prevention strategies, impact on quality of life, and treatment options for pediatric tinea capitis. Epidemiological analysis spanning from 1990 to 1993 and 2020 to 2023 reveals prevalence patterns of pediatric tinea capitis influenced by geographic, demographic, and environmental factors. Notably, Trichophyton species is most prevalent in North America; however, Microsporum species remain the primary causative agent globally, with regional variations. Risk factors include close contact and environmental conditions, emphasizing the importance of preventive measures. Accurate diagnosis relies on clinical evaluation, microscopic examination, and fungal culture. Various treatment modalities including systemic antifungals show efficacy, with terbinafine demonstrating superior mycological cure rates particularly for Trichophyton species. Recurrent infections and the potential development of resistance can pose challenges. Therefore, confirming the diagnosis, appropriately educating the patient/caregiver, accurate drug and dose utilization, and compliance are important components of clinical cure. Untreated or poorly treated tinea capitis can lead to chronic infection, social stigma, and psychological distress in affected children. Prevention strategies focus on early detection and healthy lifestyle habits. Collaborative efforts between healthcare providers and public health agencies are important in treating pediatric tinea capitis and improving patient outcomes. Education and awareness initiatives play a vital role in prevention and community-level intervention to minimize spread of infection. Future research should explore diagnostic advances, novel treatments, and resistance mechanisms in order to mitigate the disease burden effectively.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.004 |
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 teacher head, 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".