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Demographics of US Pediatric Contact Dermatitis Registry Providers

2015· article· en· W918965066 on OpenAlexvenueaboutno aff
Alina Goldenberg, Sharon E. Jacob

Bibliographic record

VenueDermatitis · 2015
Typearticle
Languageen
FieldMedicine
TopicContact Dermatitis and Allergies
Canadian institutionsnot available
FundersUniversity of Miami
KeywordsMedicineDemographicsFamily medicineQuarter (Canadian coin)Allergic contact dermatitisAllergyDemography

Abstract

fetched live from OpenAlex

BACKGROUND: Children are as likely as adults to be sensitized and reactive to contact allergens. However, the prevailing data on pediatric allergic contact dermatitis are quantitatively and qualitatively limited because of a narrow geographic localization of data-reporting providers. OBJECTIVE: The aim of the study was to present the first quarter results from the Loma Linda Pediatric Contact Dermatitis Registry focused on registered providers who self-identified as providing care for pediatric allergic contact dermatitis (ACD) within the United States. METHODS: The US providers were invited to join the registry via completion of an online, secure, 11-question registration survey addressing demographics and clinical practice essentials. The presented results reflect data gathered within the first quarter of registry recruitment; registration is ongoing. RESULTS: Of 169 responders from 48 states, the majority of providers were female (60.4%), academic (55.6%), and dermatologists (76.3%). Based on individual provider averages, the minimum cumulative number of pediatric patch-test evaluations performed each year ranged between 1372 and 3468 children. CONCLUSIONS: The Pediatric Contact Dermatitis Registry provides a description of the current leaders in the realm of pediatric ACD and gaps, which are in need of attention. The registry allows for a collaborative effort to exchange information, educate providers, and foster investigative research with the hope of legislation that can reduce the disease burden of ACD in US children.

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 score0.143
Threshold uncertainty score0.951

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 head0.019
GPT teacher head0.251
Teacher spread0.232 · 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

Citations15
Published2015
Admission routes2
Has abstractyes

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