Values and Preferences of Patients and Caregivers Regarding Treatment of Atopic Dermatitis (Eczema)
Bibliographic record
Abstract
Importance: Patient values and preferences can inform atopic dermatitis (AD) care. Systematic summaries of evidence addressing patient values and preferences have not previously been available. Objective: To inform American Academy of Allergy, Asthma & Immunology (AAAAI)/American College of Allergy, Asthma and Immunology (ACAAI) Joint Task Force on Practice Parameters AD guideline development, patient and caregiver values and preferences in the management of AD were systematically synthesized. Evidence Review: Paired reviewers independently screened MEDLINE, Embase, PsycINFO, and CINAHL databases from inception until March 20, 2022, for studies of patients with AD or their caregivers, eliciting values and preferences about treatment, rated risk of bias, and extracted data. Thematic and inductive content analysis to qualitatively synthesize the findings was used. Patients, caregivers, and clinical experts provided triangulation. The GRADE-CERQual (Grading of Recommendations Assessment, Development and Evaluation-Confidence in the Evidence from Reviews of Qualitative Research) informed rating of the quality of evidence. Findings: A total of 7780 studies were identified, of which 62 proved eligible (n = 19 442; median age across studies [range], 15 years [3-44]; 59% female participants). High certainty evidence showed that patients and caregivers preferred to start with nonmedical treatments and to step up therapy with increasing AD severity. Moderate certainty evidence showed that adverse effects from treatment were a substantial concern. Low certainty evidence showed that patients and caregivers preferred odorless treatments that are not visible and have a minimal effect on daily life. Patients valued treatments capable of relieving itching and burning skin and preferred to apply topical corticosteroids sparingly. Patients valued a strong patient-clinician relationship. Some studies presented varied perspectives and 18 were at high risk for industry sponsorship bias. Conclusions and Relevance: In the first systematic review to address patient values and preferences in management of AD to our knowledge, 6 key themes that may inform optimal clinical care, practice guidelines, and future research have been identified.
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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 | 0.041 | 0.161 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 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".