Disease Severity and Quality of Life Measurements in Contact Dermatitis: A Systematic Review 2005–2015
Bibliographic record
Abstract
BACKGROUND: Contact dermatitis (CD) has been assessed by numerous disease severity indices resulting in heterogeneity across published research. OBJECTIVE: This study aims to evaluate published CD severity scales and identify a criterion standard for assessment. METHODS: Scopus and Ovid MEDLINE were searched for human randomized controlled trials (RCTs) on CD severity measures published during a 10-year period. Eligible studies were English-language RCTs reporting disease severity outcome measures for CD in humans. Studies were excluded if they were duplicates, not available in English, not related to CD, not RCTs, not conducted on human subjects, or did not report relevant outcome measures. RESULTS: A total of 22 disease outcome measures were used in 81 included RCTs. Instrument-based measures were used in 40 (49.4%) studies, and visual assessments were used in 66 (81.5%) RCTs. Only 5 (6.2%) studies reported quality of life (QoL) outcomes. Two (2.5%) studies used a clinical severity scale, which combined both QoL and visual assessments. LIMITATIONS: This study was limited by the exclusion of non-RCTs and gray literature. CONCLUSIONS: Wide variation in CD outcome measures exists including instrument-based measures, visual assessments, and QoL outcomes. A standardized outcome measure must be generated to reduce heterogeneity.
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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 | 0.007 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".