The impact of COVD‐19 on North American dermatology practices
Bibliographic record
Abstract
COVID-19 continues to affect the delivery of healthcare services, as practices across North America gradually re-open with new safety measures and practice guidelines. Specifically in dermatology, clinical care is delivered in close physician-patient proximity through physical examination and the use of additional diagnostic and therapeutic procedures. We designed a 10-question survey to better understand how COVID-19 has impacted the delivery of care in North American dermatology practices. Survey questions explored themes including changes in patient volumes, the use of virtual visits/teledermatology, the frequency of aesthetic and surgical procedures, and other related topics. We invited 102 board-certified dermatologists working in a variety of medical, aesthetic, surgical, and mixed practices, to participate in our survey hosted through Qualtrics XM. These dermatologists were selected based on their geographic location and our ability to access their contact information. Each dermatologist received an individualized e-mail and survey link; however, all survey responses were anonymized. In 2.5 weeks after survey invitations were sent, the survey was viewed and completed by 71 and 54 dermatologists, respectively. The second wave of e-mails was sent to the remaining 48 dermatologists who had not yet completed the survey, after which 15 participants both viewed and completed the survey. In total, 69 responses were recorded with an overall response rate of 67.6%. We report decreased patient volume capacity, fewer aesthetic and surgical procedures, and an increase in the use of virtual medicine among board-certified North American dermatologists. However, this represents a reflection on perspectives at a single time point in a rapidly evolving situation. Understanding the full scope of the impact that COVID-19 continues to have on dermatologic care is paramount to effectively serve our patients.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".