A Survey of Nurse Practitioner and Physician Assistant Advanced Practice Providers Uncovers a Need for Precision Medicine in Psoriasis Management
Bibliographic record
Abstract
Objective The arrival of biologics has considerably improved the treatment of psoriasis and psoriatic arthritis; however, it can be difficult to identify which biologic(s) a patient will respond to without undergoing a trial-and-error approach. The current survey was designed to investigate biologic switching in the clinic and whether a biomarker test would assist in selecting the appropriate treatment for patients and improve psoriasis management. Methods A survey of 157 nurse practitioner and physician assistant (NP/PA) advanced practice providers was conducted to assess (1) the frequency of biologic switching and (2) the perceived clinical utility of a biomarker test that stratifies psoriasis patients to predict biologic response. Results More than half of advanced practice providers (55%) indicated that psoriasis patients require at least two different biologics to achieve an adequate response to treatment, with 59% of respondents specifying that 10% to 30% of their patients switch biologics the first year of treatment. Ninety-six percent of respondents indicated that a biomarker test would likely improve their practice, with the majority of participants (84%) suggesting a biomarker test could improve their ability to determine the most appropriate therapy for their patients. Ninety-one percent indicated they would use a biomarker test (Mind.Px, Mindera Health, San Diego, California), and 63% said they would perform the test in their office. Conclusions A biomarker test may help shift psoriasis management from a trial-and-error approach to precision care, thereby reducing the time to effective treatment and improving patient outcomes.
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".