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Record W3120807023 · doi:10.1097/der.0000000000000654

Usefulness of Patch Testing With Patient's Own Products in the Diagnosis of Allergic Contact Dermatitis

2021· article· en· W3120807023 on OpenAlexvenueno aff
Fátima Tous‐Romero, Pablo L. Ortiz‐Romero, F.J. Ortiz de Frutos

Bibliographic record

VenueDermatitis · 2021
Typearticle
Languageen
FieldMedicine
TopicContact Dermatitis and Allergies
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineDermatologyAllergic contact dermatitisPatch testingCosmeticsContact dermatitisPatch testAllergenAllergyPathologyImmunology

Abstract

fetched live from OpenAlex

BACKGROUND: The usefulness of using patient's own products in patch tests for the diagnosis of allergic contact eczema is well known. However, most of the literature is based on case series published decades ago, and they are focused on cosmetics and fragrances. OBJECTIVE: The aim of the study was to evaluate the usefulness of using patient's own products in patch tests for the diagnosis of contact eczema in a contact dermatitis unit, describing the most frequently positive own products, as well as the most frequently responsible allergens. METHODS: In a 17-year period, 3514 patients were patch tested in our department. In 2429 patients, patch testing with the patients' own products was performed. RESULTS: We found that 363 patients (10.33%) reacted to their own products. In 131 cases (3.81%), reacting to their own product was the only clue for detecting the responsible allergen for allergic contact eczema. Most reactions were found for topical medications, moisturizers, and adhesives. Fragrance mix I, methylchloroisothiazolinone/methylisothiazolinone, ketoprofen, and colophony were found to be the allergens most often responsible. CONCLUSIONS: It is essential to include patient's own products in the study of allergic contact eczema to make a correct diagnosis. In our series, 3.81% of the patients would not have been correctly diagnosed if their own products had not been included in patch tests.

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 score0.075
Threshold uncertainty score0.580

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.029
GPT teacher head0.235
Teacher spread0.206 · 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

Citations10
Published2021
Admission routes1
Has abstractyes

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