Demographics of US Pediatric Contact Dermatitis Registry Providers
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".