Systematic review of the use of the Dermatology Life Quality Index in routine clinical practice: evidence from 287 articles across 56 countries
Bibliographic record
Abstract
BACKGROUND: Although quality of life instruments are widely used in research, it is challenging to find evidence of their use in routine clinical use. The most widely used measure for skin disease burden is the Dermatology Life Quality Index (DLQI), and its scores have validated clinical meaning. OBJECTIVES: To identify evidence of the use of the DLQI in routine clinical practice and explore the nature of its use. METHODS: The study followed PRISMA guidelines, and the protocol was registered with PROSPERO. MEDLINE (Ovid), Embase, Scopus and CINAHL (EBSCO) databases were systematically searched for articles describing studies using the DLQI in routine clinical practice. Studies were excluded if participants were aged < 16 years and if there were predetermined treatment interventions, as in a clinical trial. Information was extracted on publications' authors' opinions on the use of the DLQI in their routine practice. RESULTS: In total, 2178 publications were screened and 287 articles met the inclusion criteria, reporting on 112 diseases and describing 66 434 patients from 56 countries, using the DLQI in at least 29 languages. Of the studies, 121 (42.2%) were reported as retrospective and 63 (22.0%) as observational. Fifty-two (18.1%) stated DLQI data were retrieved from patient records, 29 (10.1%) as 'real life', 39 (13.6%) reported 'real-world data' and 47 (16.4%) used consecutive patient recruitment. In total, 264 (92.0%) studies were conducted in a single country; 96 (33.4%) were multicentred studies, whereas 171 (59.6%) were conducted at a single site. There were 93 (32.4%) that were conducted in hospitals, 66 (23.0%) specified outpatient clinics, 38 (13.2%) tertiary care, 33 (11.5%) clinics, 4 (1.4%) in the community, 18 (6.3%) in other settings and 35 (12.2%) were unspecified. The most common diseases in the study settings were psoriasis (106 studies, 36.9%), atopic dermatitis (32, 11.1%), urticaria (24, 8.4%), hidradenitis suppurativa (22, 7.7%) and vitiligo (17, 5.9%). Thirty studies (10.5%) used Hongbo et al.'s (J Invest Dermatol 2005; 125:659-64) DLQI score banding. CONCLUSIONS: The DLQI was widely used in routine care locations internationally, informing clinical decisions and monitoring of treatment. The DLQI was embedded into some clinics' continuing routine practice.
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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 | 0.023 | 0.107 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.007 |
| Bibliometrics | 0.027 | 0.031 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".