The differences of demographic features and seasonal variation in Seborrheic Dermatitis Between Adults And Children: A Cross-Sectional, Single-Center, Hospital-Based Study
Bibliographic record
Abstract
Objective: Seborrheic dermatitis (SD) is a common chronic inflammatory skin disorder characterized by erythematous papulosquamous lesions in body regions that are rich in sebaceous glands, particularly the scalp, face, and intertriginous areas.Seasonal variation has been reported in different skin diseases; however, the results have been contradictory.The aim of this research was to analyze the demographic characteristics of patients with SD and to examine the impact of seasonality.Methods: Patients who were diagnosed with SD at an outpatient dermatology clinic between June 1, 2015 and June 1, 2020 were included in this retrospective, cross-sectional, single-center, hospital-based study.Both pediatric and adult dermatology outpatient cases were evaluated.Results: A total of 2656 patients with SD presented at the outpatient clinic during the study period.The mean age was 31.99±15.88years (range: 0-87 years).Among these patients, 1540 (58%) were males and 1116 (42%) were females.SD was most common among those aged 20-30 years (29.9%).The mean age was 29.51±15.43years in the female patients and 33.80±15.96 in males.Evaluation according to season revealed that 817 (31%) patients presented during the winter months, 615 patients in spring (23%), 452 in summer (17%), and 772 (29%) in the autumn.Conclusion: SD is a common, multifactorial skin disease.This study evaluated a wide patient profile and it was observed that middle-aged adults and men were the most affected.Patients presented most frequently during the cold weather season.Although the data obtained from this study are similar to others in the existing literature, these findings provide a new contribution to the epidemiological data of Turkey.Prospectively designed, population-based studies that evaluate potential triggering factors will help to explain the full etiology and seasonal incidence more clearly.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".