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Record W4392793920 · doi:10.1089/derm.2023.0365

Patient and Caregiver Perspectives on the Relationship between Atopic Dermatitis Symptoms and Mental Health

2024· article· en· W4392793920 on OpenAlexvenueno aff
Jessica K. Johnson, Allison R. Loiselle, Sheena Chatrath, Wendy Smith Begolka

Bibliographic record

VenueDermatitis · 2024
Typearticle
Languageen
FieldMedicine
TopicDermatology and Skin Diseases
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineAtopic dermatitisDepression (economics)AnxietyMental healthDiseasePsychiatryClinical psychologyPerceptionDermatologyInternal medicinePsychology

Abstract

fetched live from OpenAlex

Abstract: Background: Atopic Dermatitis (AD) patients have increased likelihood of developing depression and anxiety. The patient and caregiver's perceptions of the correlation of mental health (MH) and AD symptoms are not well understood. Objective: To evaluate patient-reported MH symptoms and their correlation with AD disease severity and understand patient-perceived associations of AD with impacts to their MH. Methods: Adult AD patients (18+ years) or caregivers of AD patients (8–17 years) were recruited to complete a survey about MH and their perception of its relationship with AD. Results: Of 1496 respondents, 954 met inclusion criteria and completed the survey. Respondents were primarily adults (83.3%) with moderate AD (31.4%). In total, 26.0% reported MH symptoms >10 days per month, and most adults (65.5%) scored in the borderline/abnormal range on the Hospital Anxiety and Depression Scale. Most (70.6%) respondents perceived their MH was negatively affected by AD in the past 12 months. AD severity impacted the perception of the relationship between AD and MH; respondents were more likely to believe MH was impacted by AD when they/their child had severe AD. Conclusion: Our study highlights patient and caregiver awareness of the detrimental impact of AD on MH. Addressing MH in AD care settings early in the disease journey may be beneficial.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.185
Threshold uncertainty score0.318

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.016
GPT teacher head0.276
Teacher spread0.260 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations8
Published2024
Admission routes1
Has abstractyes

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