Trends in Patch Testing by Health Care Providers among US Medicare Beneficiaries
Bibliographic record
Abstract
BACKGROUND: Although patch testing has historically been done by dermatologists, allergists are also patch testing. Little is known about the current utilization of patch testing by medical specialists. OBJECTIVE: The aim was to determine trends in utilization of patch testing in Medicare beneficiaries by various clinicians and demographics. METHODS: Data from the 2012-2017 Medicare Public Use File were analyzed, including 82,241 total unique clinicians of whom 312 filed a patch testing claim. RESULTS: Dermatologists had a steady share of patch tests (annual clinicians; annual patches) from 2012 (158; 258,735) to 2017 (199; 351,994), an increase of 25.9% and 36.0%, respectively. Allergists, however, had a marked increase in utilization of patch tests from 2012 (84; 62,498) to 2017 (187; 182,480), an increase of 122.6% and 192.0%, respectively. In multivariable logistic regression models, male dermatologists and allergists had increased odds of patch testing (P < 0.001 for both), as did clinicians in the Northeast and Southern United States (P ≤ 0.003 for both). LIMITATIONS: Data are only available for Medicare Part B patients; changes in utilization may be different for individuals, private insurance, or Medicare Advantage Plans. CONCLUSIONS: Relative to dermatologists, patch testing is increasing among allergists. Addressing barriers to patch testing may increase rates of patch testing by dermatologists.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.003 | 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".