Psychometric Properties of the Instrument for Scoring Clinical Outcomes of Research for Epidermolysis Bullosa patient score (iscorEB-p): a patient-reported outcome measure
Bibliographic record
Abstract
BACKGROUND: In contrast to clinical diagnosis via external examination, patient-related outcome measures (PROMs) allow access to patients' internal perceptions. In the case of epidermolysis bullosa (EB) - a rare disease characterized by a wide variety of symptoms and individual disease courses - it is important to integrate the patient's perspective into diagnostic processes. The Instrument for Scoring Clinical Outcomes of Research for EB (iscorEB) is an EB-specific measurement tool, combining a clinician score (iscorEB-c) and a patient questionnaire (iscorEB-p). OBJECTIVES: The aim of this study is to establish the iscorEB-p as an independent PROM tool by exploring its psychometric properties. METHODS: Sample-based psychometric testing and evaluation were performed on data collected via a multinational online cross-sectional study. RESULTS: Data analysis was performed with n = 95 participants across all EB types. The reliability and internal consistency of the iscorEB-p was excellent (α = 0.90). Principal component analysis with a varimax rotation resulted in a two-factor solution, explaining 55.6% of the total variance, with the distinct factors 'everyday life functioning' and 'specific EB symptoms'. Convergent validity was shown by high correlations to the Satisfaction With Life Scale (r = -0.52, P < 0.001), the Quality of Life in Epidermolysis Bullosa questionnaire (r = 0.72, P < 0.001) and the Epidermolysis Bullosa Family Burden of Disease questionnaire (r = -0.73, P < 0.001). CONCLUSIONS: The iscorEB-p is a reliable and valid instrument to assess patient-reported health status of people with EB.
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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 | 0.018 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".