Predictors of family impact of juvenile localized scleroderma
Bibliographic record
Abstract
OBJECTIVE: To measure the impact juvenile localized scleroderma (jLS) has on family quality of life and to identify predictors of family impact in this population which may inform the development of tailored resources to enhance family functioning for patients with jLS. METHODS: A retrospective cohort study of pediatric patients with jLS and their families was conducted. Five questionnaires were administered at each visit: Pediatric Quality of Life Inventory Family Impact Module (PedsQL-FIM), PedsQL 4.0 Generic Core Scales (PedsQL-Generic), PedsQL Rheumatology Module (PedsQL-RM), Child Health Assessment Questionnaire (CHAQ), and Children's Dermatology Life Quality Index (CDLQI). Linear mixed models with random intercepts for each patient were used to find relationships between family impact scores and clinically relevant variables over time. Variables of interest included disease activity status, methotrexate use, jLS distribution, and scores for PedsQL-Generic and PedsQL-RM. RESULTS: The median baseline PedsQL-FIM total score was 80.9 (IQR = 76.6-97.4). Adjusting for age and sex, the most significant predictors of family impact were PedsQL-Generic scores and four of five PedsQL-RM dimensions (all P < .001); methotrexate use had borderline significance (P = .06). Family impact increased more significantly over time in older patients. In multivariable modeling, PedsQL-Generic total score and jLS "other" distribution were significant for predicting an increased PedsQL-FIM score (P = .003 and P = .03, respectively). CONCLUSIONS: JLS has a moderate family impact. Family impact is predicted by patients' general and disease-specific health-related quality of life (HRQL) and their jLS subtype. There is a trend toward increased family impact with methotrexate treatment. This study emphasizes the importance of family-centered care in jLS.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
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 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".