Evaluation of the measurement properties of symptom measurement instruments for atopic eczema: a systematic review
Bibliographic record
Abstract
BACKGROUND: Symptoms have been identified as a core outcome domain for atopic eczema (AE) trials. Various instruments exist to measure symptoms in AE, but they vary in quality and there is a lack of standardization between clinical trials. Our objective was to systematically evaluate the quality of the evidence on the measurement properties of AE symptom instruments, thereby informing consensus discussions within the Harmonising Outcome Measures for Eczema (HOME) initiative regarding the most appropriate instruments for the core outcome domain symptoms. METHODS: Using the COnsensus-based Standards for the selection of health Measurement INstruments (COSMIN) checklist and predefined criteria for good measurement properties on identified development and validation studies of AE symptom instruments, a best evidence synthesis was performed to draw an overall conclusion on quality of the instruments and to provide recommendations. RESULTS: Eighteen instruments were identified and evaluated. When the quality and results of the studies were considered, only five of these instruments had sufficient validation data to consider them for the core outcome set for the core outcome domain symptoms. These were the paediatric Itch Severity Scale (ISS), Patient-Oriented Eczema Measure (POEM), Patient-Oriented SCOring Atopic Dermatitis (PO-SCORAD), Self-Administered Eczema Area and Severity Index (SA-EASI) and adapted SA-EASI. CONCLUSIONS: ISS (paediatric version), POEM, PO-SCORAD, SA-EASI and adapted SA-EASI are currently the most appropriate instruments and therefore have the potential to be recommended as core symptom instrument in future clinical trials. These findings will be utilized for the development of a core outcome set for AE.
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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 | 0.049 | 0.176 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.012 |
| Bibliometrics | 0.015 | 0.014 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".