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Record W3005998933 · doi:10.1097/der.0000000000000542

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

2020· article· en· W3005998933 on OpenAlexvenueno aff
Jodie Raffi, Isabel Elaine Allen, Nina Botto

Bibliographic record

VenueDermatitis · 2020
Typearticle
Languageen
FieldMedicine
TopicContact Dermatitis and Allergies
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineQuality of life (healthcare)Allergic contact dermatitisPatch testingPopulationPhysical therapyContact dermatitisAllergyImmunologyEnvironmental healthNursing

Abstract

fetched live from OpenAlex

BACKGROUND: Although many generic dermatological quality-of-life (QoL) instruments exist, none have been specifically designed for patients with allergic contact dermatitis (ACD). In the preceding publication-Validating a Quality-of-Life Instrument for Allergic Contact Dermatitis-we developed and validated a QoL instrument specific to the ACD population. OBJECTIVE: The aim of this study was to assess whether this ACD-specific QoL instrument appropriately captures change in QoL after patch testing in ACD patients. METHODS: One hundred individuals completed the previously validated 17-item QoL survey plus 2 global questions and the Skindex-29 before patch testing. Two months after patch testing and allergen avoidance, the participants repeated the same questionnaires. We used statistical methods to evaluate the capacity of the ACD questionnaire to measure change in QoL in comparison with the Skindex-29. CONCLUSIONS: The novel ACD-specific questionnaire was more sensitive to change in QoL than the generic Skindex-29. Eleven of the original 17 items were found to capture change in QoL, and of the 3 domains (emotions, symptoms, functioning), the emotional aspect of the disease was most burdensome and responsive to change 2 months after patch testing. Providers can reliably use this index to assess changes in QoL over time.

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)0.001
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 score0.447
Threshold uncertainty score0.846

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0000.000

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 head0.067
GPT teacher head0.317
Teacher spread0.250 · 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

Citations13
Published2020
Admission routes1
Has abstractyes

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