Instrument for Scoring Clinical Outcome of Research for Epidermolysis Bullosa: A Consensus‐Generated Clinical Research Tool
Bibliographic record
Abstract
Epidermolysis bullosa (EB) is a genetic condition characterized by skin fragility and blistering. There is no instrument available for clinical outcome research measurements. Our aim was to develop a comprehensive instrument that is easy to use in the context of interventional studies. Item collection was accomplished using a two-step Delphi Internet survey process for practitioners and qualitative content analysis of patient and family interviews. Items were reduced based on frequency and importance using a 4-point Likert scale and were subject to consensus (>80% agreement) using the nominal group technique. Pilot data testing was performed in 21 consecutive patients attending an EB clinic. The final score, Instrument for Scoring Clinical Outcome of Research for Epidermolysis Bullosa (iscorEB), is a combined score that contains clinician items grouped in five domains (skin, mucosa, organ involvement, laboratory abnormalities, and complications and procedures; maximum score 114) and patient-derived items (pain, itch, functional limitations, sleep, mood, and effect on daily and leisurely activities; maximum score 120). Pilot testing revealed that combined (see below) and subscores were able to differentiate between EB subtypes and degrees of clinical severity (EB simplex 21.7 ± 16.5, junctional EB 28.0 ± 20.7, dystrophic EB 57.3 ± 24.6, p = 0.007; mild 17.3 ± 9.6, moderate 41.0 ± 19.4, and severe 64.5 ± 22.6, p < 0.001). There was high correlation between clinician and patient subscores (correlation coefficient = 0.79, p < 0.001). iscorEB seems to be a sensitive tool in differentiating between EB types and across the clinical spectrum of severity. Further validation studies are needed.
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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 | 0.033 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".