Cross‐sectional quantitative validation of the paediatric Localized Scleroderma Quality of Life Instrument (<scp>LoSQI</scp>): A disease‐specific patient‐reported outcome measure
Bibliographic record
Abstract
BACKGROUND: The Localized Scleroderma Quality of Life Instrument (LoSQI) is a disease-specific patient-reported outcome (PRO) measure designed for children and adolescents with localized scleroderma (LS; morphea). This tool was developed using rigorous PRO methods and previously cognitively tested in a sample of paediatric patients with LS. OBJECTIVE: The purpose of this study was to evaluate the psychometric properties of the LoSQI in a clinical setting. METHODS: Cross-sectional data from four specialized clinics in the US and Canada were included in the analysis. Evaluation included reliability of scores, internal structure of the survey, evidence of convergent and divergent validity, and test-retest reliability. RESULTS: One hundred and ten patients with LS (age: 8-20 years) completed the LoSQI. Both exploratory and confirmatory factor analysis supported the use of two sub-scores: Pain and Physical Functioning, and Body Image and Social Support. Correlations with other PRO measures were consistent with pre-specified hypotheses. LIMITATIONS: This study did not evaluate longitudinal validity or responsiveness of scores. CONCLUSION: Results from a representative sample of children and adolescents with LS continue to support the validity of the LoSQI when used in a clinical setting. Future work to evaluate the responsiveness is ongoing.
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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 | 0.012 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".