Measurement Properties of Outcome Measures for Vitiligo
Bibliographic record
Abstract
OBJECTIVE To summarize and critically appraise the evidence on the measurement properties of clinician-, patient-, and observer-reported outcomes, measuring any construct of interest in patients with all types of vitiligo. DATA SOURCES Electronic databases including PubMed (1948 to July 2011), OVID EMBASE (1980 to July 2011), and CINAHL (EBSCOhost) (1982 to July 2011) were searched. STUDY SELECTION Two authors independently screened all records for eligibility. For inclusion, the study population had to include patients with vitiligo, for which outcome measures were developed or evaluated on their measurement properties. The initial search retrieved 1249 records, of which 14 articles met the inclusion criteria. DATA EXTRACTION Characteristics of the included instruments, study population, and results of the measurement properties were extracted. The Consensus-Based Standards for the Selection of Health Status Measurement Instruments (COSMIN) 4-point checklist, combined with quality criteria for measurement properties, was used to calculate the overall level of evidence per measurement property of each instrument. Independent extraction and assessment was performed by 2 authors. DATA SYNTHESIS Eleven different measurement instruments were identified. Strong evidence was found for a positive internal consistency of the Dermatology Life Quality Index. For other instruments, the evidence of measurement properties was limited or unknown. CONCLUSIONS Recommendations on the use of specific outcome measures for vitiligo should be formulated with caution because current evidence is insufficient owing to a low number of studies with poor methodological quality and unclear clinical relevance. To recommend outcome measures for vitiligo, further research on measurement properties of clinical relevant outcome measures for vitiligo according to COSMIN quality criteria is 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 | 0.191 | 0.506 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.013 |
| Bibliometrics | 0.015 | 0.014 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".