Unmet Needs of Effective Advanced Systemic Therapies in Moderate-to-Severe Atopic Dermatitis Patients in the TARGET-DERM AD Registry
Bibliographic record
Abstract
In the United States, 40-50% of patients with atopic dermatitis (AD) have moderate-to-severe disease, often necessitating advanced systemic therapies (ASTs; biologics or Janus kinase inhibitors). TARGET-DERM AD is an observational, longitudinal registry that tracks the natural history and treatment of AD, including patients with moderate-to-severe disease. Among enrollees, we defined 4 patient subgroups: AST-Naïve, AST-Retrospective (AST initiated prior to enrollment), AST-Prospective (AST initiated at or after enrollment), and AST-Failed (failed at any point). This analysis describes AST-patient demographics, treatment patterns, and longitudinal outcomes. Of 598 qualifying participants (22% adolescent, 78% adult), 34% were AST-Naive, 27% AST-Retrospective, 31% AST-Prospective, and 8% AST-Failed. Comparing the adult subgroups showed significant differences in enrollment age, and race/ethnicity, but not among adolescents. There was no significant difference in AST prescription rates. Literature-based validated thresholds were used to define unchanged or worsening for each outcome, which was combined into a single category, "lacked improvement." At 52 weeks of AST, AST-Prospective adolescents lacked improvement on Validated Investigator's Global Assessment of Atopic Dermatitis (vIGA-AD) (26%), body surface area (BSA) (34%), Numeric Rating Scale (NRS)-Pain (63%), and NRS-Sleep (52%); AST-Prospective adults lacked improvement on vIGA-AD (21%), BSA (51%), NRS-Pain (66%), and NRS-Sleep (60%). As one-third of participants did not progress to AST, and noteworthy proportions of patients lacked improvement, this study highlights unmet needs and treatment inadequacies in patients with moderate-to-severe AD.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".