Trends in Patch Testing in the Medicare Part B Fee-for-Service Population
Bibliographic record
Abstract
BACKGROUND: Patch testing is a vital component of the workup for allergic contact dermatitis. There are limited data on changes of patch testing use among Medicare providers, as well as patch testing reimbursement rates. OBJECTIVE: The aim of the study was to evaluate trends in the use of patch testing among various Medicare providers and Medicare patch testing reimbursement. DESIGN: A longitudinal analysis of patch testing claims was performed with the Medicare Part B Physician/Supplier Procedure Summary files from 2010 to 2018. The primary outcomes were the total number and change in the number of submitted patch testing services from 2010 to 2018 by 3 provider groups: dermatology physicians, nondermatology physicians, and nonphysician providers. Secondary outcome measures included Medicare reimbursement amounts and changes in reimbursement amounts for patch test services (total and per 1000 enrollees) from 2010 to 2018 for the 3 provider groups, as well as per patch test service. RESULTS: From 2010 to 2018, submitted patch testing services per 1000 enrollees grew by 89.0%. The annual trend estimate for submitted services relative to 2010 was +10.1% (95% confidence interval [CI] = 8.1 to 12.0) for physicians and +34.1% (95% CI = 32.1 to 36.0) for nonphysician providers (physician assistants and nurse practitioners). Among physicians, the annual trend estimate for submitted services was +5.1% (95% CI = -11.3 to 21.5) for dermatologists and +31.40% (95% CI = 15.00 to 47.81) for allergists. CONCLUSIONS: Patch testing increased in the US Medicare population from 2010 to 2018, and this increase was largely driven by nonphysician providers and allergists.
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 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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".