Participation in Clinical Trials Among Academic Dermatologists Affiliated With Veterans Affairs Hospitals: Survey Study
Bibliographic record
Abstract
Background: Clinical trials have led to the development of new and effective therapies for many dermatologic conditions. To our knowledge, there is no published study that has quantified and described the degree of involvement in clinical trials among academic dermatologists and their university affiliates. Objective: The purpose of this study was to characterize the involvement of academic dermatology departments in clinical trials research. Methods: An online survey was sent to 211 Veterans Affairs (VA)-employed dermatologists. It comprised 20 questions related to the number of clinical trials, support staff dedicated to clinical research, skin diseases studied, and the effect of the COVID-19 pandemic on conducting clinical research. Three rounds of survey invitations were sent over a 3-month period (March to May 2021). Data from all survey responses were reviewed for quantitative and descriptive analyses of the key outcome measures. Results: A total of 48 dermatologists completed the survey and provided their university affiliations and details of involvement in clinical trials research. Over half of participants (n=25, 58.1%) with a university affiliate reported that their affiliated dermatology department had a dedicated clinical trials unit. Basal cell carcinoma was the most frequently studied skin condition (n=9, 18.8%), followed by atopic dermatitis and psoriasis (n=4, 8.3% each); 66.7% of participants reported no current clinical trials participation. Of those conducting clinical trials, 87% (n=18) noted that COVID-19 was a barrier to conducting trials, with 52.2% (n=11) citing disrupted or decreased trials due to the pandemic. Conclusions: Although many dermatologists with university affiliations reported having a dedicated clinical trials unit at their institution, a majority of those surveyed reported not taking part in any active trials. Overall, the diseases investigated in academic clinical trials appear to follow national trends, though some of the top dermatological diseases are underrepresented in clinical trials research. A key limitation of our study was the low response rate (~23%) and that the survey responses from the sample of VA-based dermatologists may not be generalizable to all academic dermatology departments in the United States. The effect of the COVID-19 pandemic appeared to play a significant role in disrupting active trials.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.056 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".