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Record W2412640343 · doi:10.1097/der.0000000000000132

Clinical Utilization of Repeated Open Application Test Among American Contact Dermatitis Society Members

2015· article· en· W2412640343 on OpenAlexvenueno aff
Gabrielle Brown, Nina Botto, Daniel Butler, Jenny E. Murase

Bibliographic record

VenueDermatitis · 2015
Typearticle
Languageen
FieldMedicine
TopicContact Dermatitis and Allergies
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineTest (biology)Contact dermatitisPatch testingDermatologyImmunologyAllergy

Abstract

fetched live from OpenAlex

BACKGROUND: The repeated open application test (ROAT) provides useful information regarding allergens in suspected cases of allergic contact dermatitis; however, standardized methodology has not been established. OBJECTIVE: The aim of this study was to assess how ROAT is used in clinical and research settings. METHODS: We distributed a survey regarding ROAT practice to the American Contact Dermatitis Society and conducted a literature review of ROAT utilization in research. RESULTS: A total of 67 American Contact Dermatitis Society members participated in the survey. Respondents most frequently recommend application of leave-on products twice daily (46.0%) and rinse-off products once daily (43.5%). The most commonly used anatomical sites include the forearm (38.7%) and antecubital fossa (32.3%). Most respondents continue ROAT for 1 (49.2%) or 2 weeks (31.7%). Literature review of 32 studies (26 leave-on, 6 rinse-off) revealed that application frequency is most common at twice daily for both leave-on (96.2%) and rinse-off (50.0%) products. The most common anatomical site is the forearm (62.5%), with an overall study duration of 3 to 4 weeks (65.6%). CONCLUSIONS: When comparing ROAT clinical and research practice, the majority trend was consistent for leave-on product application frequency and anatomical site, but not for rinse-off product application frequency, or overall duration. Further research is needed to determine best practice recommendations.

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 score0.116
Threshold uncertainty score0.802

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)0.0000.000

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 head0.053
GPT teacher head0.351
Teacher spread0.297 · 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

Citations12
Published2015
Admission routes1
Has abstractyes

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