Association between filaggrin gene mutations and the clinical features of molluscum contagiosum: The Yamanashi Adjunct Study of the Japan Environment and Children's Study
Bibliographic record
Abstract
Previous studies have reported swimming, atopic dermatitis, and filaggrin (FLG) gene mutations as risk factors for molluscum contagiosum (MC) infection. FLG gene mutations impair skin barrier function. The aim of this study was to determine the impact of FLG mutations on the incidence and clinical features of MC. We used data from 2036 children who participated in the Yamanashi Adjunct Study of the Japan Environment and Children's Study, a prospective, birth cohort study. A questionnaire for caregivers (when children were 4 and 8 years of age) asked about clinical features including previous MC incidence and treatment, number of MC lesions at first visit, and time to resolution. Participants underwent genotyping to detect six FLG mutations that are common in the Japanese population. A logistic regression model was used to analyze the association between MC incidence and FLG mutations, adjusted for potential confounders. The cumulative incidence of MC at age 8 years was 47.1%. Among participants with a history of MC, 67.6% had undergone curettage. FLG mutation was a significant risk factor for MC incidence (adjusted odds ratio [aOR] 1.69, 95% confidence interval [CI] 1.18-2.42). Swimming and atopic dermatitis were also significant risk factors for MC. There was no significant association between FLG mutation and the number of MC lesions at the first visit or the time to resolution of lesions. FLG mutation is a risk factor for MC incidence; however, FLG mutations do not affect the number of MC lesions at presentation or the time to resolution.
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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".