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Record W2920419048 · doi:10.1097/der.0000000000000452

Effect of Patch Testing on the Course of Allergic Contact Dermatitis and Prognostic Factors That Influence Outcomes

2019· article· en· W2920419048 on OpenAlexvenueno aff
Pınar Korkmaz, Ayşe Boyvat

Bibliographic record

VenueDermatitis · 2019
Typearticle
Languageen
FieldMedicine
TopicContact Dermatitis and Allergies
Canadian institutionsnot available
Fundersnot available
KeywordsMedicinePatch testContact dermatitisAllergic contact dermatitisQuality of life (healthcare)Patch testingDermatologyAllergenHand dermatitisInternal medicineAllergyImmunology

Abstract

fetched live from OpenAlex

BACKGROUND: Allergic contact dermatitis (ACD) has been shown to adversely affect the quality of life of patients. OBJECTIVE: The aim of the study was to study the effect of patch test on the severity of dermatitis, the quality of life of patients, and the prognostic factors influencing the outcome. METHODS: The study included 111 patients patch tested with the preliminary diagnosis of ACD. Patients with clinically relevant positive patch test reactions were included in the ACD group. All patients were assessed with the Investigator Global Assessment and the Dermatology Quality of Life Index before and 6 months after patch testing. RESULTS: At the sixth-month control, more significant regressions in the mean Investigator Global Assessment and Dermatology Quality of Life Index scores were noted in the ACD group. The allergens were correctly remembered by 75% of the patients. The improvement was more significant in patients with ACD who correctly remembered the allergens and made appropriate lifestyle changes. Multiple allergen positivity was identified as a poor prognostic factor. CONCLUSIONS: The effect of patch test on the prognosis of contact dermatitis depends not only on providing necessary information to patients but also on the number of positive reactions, patient's ability to recall the allergens, how much the avoidance was achieved, and patient-related factors such as sex.

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 score0.020
Threshold uncertainty score0.610

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 head0.014
GPT teacher head0.257
Teacher spread0.243 · 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

Citations23
Published2019
Admission routes1
Has abstractyes

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