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Record W2973096175 · doi:10.1097/der.0000000000000520

Real-World Outpatient Prescription Patterns for Atopic Dermatitis in the United States

2019· article· en· W2973096175 on OpenAlexvenueno aff
Partik Singh, Jonathan I. Silverberg

Bibliographic record

VenueDermatitis · 2019
Typearticle
Languageen
FieldMedicine
TopicDermatology and Skin Diseases
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineAtopic dermatitisMedical prescriptionDermatologyFamily medicinePharmacology

Abstract

fetched live from OpenAlex

Atopic dermatitis (AD) often requires combination treatment regimens. However, little is known about treatment combinations and polypharmacy in AD. We sought to characterize patterns of outpatient prescriptions and polypharmacy among US children and adults with AD. Data from the 1993-2015 National Ambulatory Medical Care Survey were analyzed, including 128,300 pediatric and 623,935 adult outpatient visits. Among AD visits, dermatologists prescribed more topical corticosteroids (TCSs, P = 0.01) than any other clinicians, particularly multiple TCSs (P < 0.0001), topical calcineurin inhibitors (TCI, P = 0.009), combination TCIs with TCSs (P = 0.004), and systemic immunosuppressants (P = 0.003). Prescriptions for multiple TCSs increased from ages 0 to 19 years, 20 to 39 years, and peaked at 40 to 59 years (P = 0.0002). Prescriptions for prednisone peaked at ages of 40 to 59 years (P = 0.003). A subset of AD patients was prescribed oral antibiotics (7.1%), although fewer than half had a diagnosis of bacterial infection (42.1%). The proportion of patients receiving multiple prescriptions was higher in visits to primary care practitioners versus dermatologists, those with private versus public insurance, and 50 years or older versus 20 to 49 years versus 0 to 19 years. Visits with 4 or more prescriptions by dermatologists increased between 1993-2000 (10%) and 2011-2015 (29%, P = 0.0001). In conclusion, significant treatment variation exists among specialists managing AD, with increasing polypharmacy over time.

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 score0.017
Threshold uncertainty score0.396

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.017
GPT teacher head0.272
Teacher spread0.255 · 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

Citations17
Published2019
Admission routes1
Has abstractyes

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