Not All Itch Is Created Equal in Atopic Dermatitis: A Prospective Observational Study of the Heterogeneous Characteristics of Itch
Bibliographic record
Abstract
Abstract: Background: Although itch is the universal symptom of atopic dermatitis (AD), little is known about its clinical phenotypes in AD. Objective: To examine the relationships of itch characteristics and patterns with AD severity, mental health, sleep, and quality of life (QOL). Methods: We conducted a prospective study of 456 adult patients with AD using validated questionnaires and skin examinations. Thirteen itch characteristics were assessed with the Patient-Reported Outcomes Measurement Information System Itch-Quality. Multivariable regression models evaluated the associations of itch characteristics, severity, or patterns with outcomes. Results: A total of 283 (62%) patients described their itch with ≥1 descriptor—most commonly, stinging (29%), tingling (24%), burning (23%), and painful (23%). The number of itch characteristics reported and endorsement of at least one itch quality were associated with greater AD and itch severity, history of anxiety and depression, active symptoms of clinical depression, sleep impairment, and QOL decrement. Reporting ≥1 itch characteristic remained associated with anxiety, active depression, sleep disturbance, and QOL impact independent of itch severity. All 13 itch descriptors were associated with worse sleep and QOL even after controlling for itch severity. Dermatitis affecting the face, eyelids, hands, or feet was each associated with distinct patterns of itch qualities. Latent class analysis identified three phenotypes of itch, which correlated with AD and itch severity, active symptoms of depression, and QOL decrement. Conclusion: Adults with AD commonly describe their itch heterogeneously using pain-related sensory descriptors. The quality of itch may independently predict mental health outcomes, sleep impairment, and QOL impact.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".