Real-World Outpatient Prescription Patterns for Atopic Dermatitis in the United States
Bibliographic record
Abstract
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.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".