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Record W2972467570 · doi:10.1097/der.0000000000000515

Validating a Quality-of-Life Instrument for Allergic Contact Dermatitis

2019· article· en· W2972467570 on OpenAlexvenueno aff
Nina Botto, Jodie Raffi, Megha Trivedi, Faustine D. Ramirez, Isabel Elaine Allen, Mary‐Margaret Chren

Bibliographic record

VenueDermatitis · 2019
Typearticle
Languageen
FieldMedicine
TopicContact Dermatitis and Allergies
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineAllergic contact dermatitisQuality of life (healthcare)Reliability (semiconductor)PopulationContact dermatitisDiseaseAllergyPathologyNursingEnvironmental healthImmunology

Abstract

fetched live from OpenAlex

BACKGROUND: Contact dermatitis is a prevalent condition that has a significant impact on quality of life (QoL). Although many generic dermatological QoL instruments exist, none were developed by and for patients with allergic contact dermatitis (ACD). OBJECTIVE: The aim of the study was to create and validate a reliable QoL instrument specific for the ACD population. METHODS: We identified QoL items specific to ACD through a series of qualitative interviews with ACD patients and experts. We created a 17-question survey that queries the patient across the following 3 major domains: symptoms, functioning, and emotions. We used statistical methods to evaluate the reliability and validity of this tool. RESULTS: Ninety patients with relevant positive results on patch testing completed the novel ACD instrument and the Skindex-29. This instrument exhibited reliability and validity in individuals with ACD and was more sensitive than the generic tool Skindex-29. CONCLUSIONS: This novel instrument is the first tool developed specifically to assess the unique impacts of ACD on QoL. Providers can reliably use this index to assess the specific aspects of the disease most problematic for the ACD patient and use this information to more properly inform counseling and management.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.126
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0010.000

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 head0.039
GPT teacher head0.302
Teacher spread0.262 · 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.

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

Citations12
Published2019
Admission routes1
Has abstractyes

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