ArticleJAMA dermatology2018
Comparison of Dermatologist Density Between Urban and Rural Counties in the United States.
Article in JAMA dermatology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06463860 (Retrospective, Multi-Center Study to Evaluate the Performance of DermDx as an Adjunctive Tool for Primary Care Physicians in the Detection of Skin Cancers), which is not on this map. Cited by 61 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Retrospective, Multi-Center Study to Evaluate the Performance of DermDx as an Adjunctive Tool for Primary Care Physicians in the Detection of Skin Cancers
Who cites it
61 citing papers in PubMed.
- The Impact of Initial Biopsy Modality and Provider Credentials on Upstaging and Management Changes in Cutaneous Melanoma.Annals of surgical oncology · 2026Article
- Inequality and determinants of access to phototherapy among patients with psoriasis in Thailand.Skin health and disease · 2026Article
- The Effects of Outreach Initiatives on Equitable Access to Dermatology Services: Single-Center Retrospective Analysis.JMIR dermatology · 2026Article
- Artificial intelligence in dermatology: A literature review of current evidence and clinical implementation.JID innovations : skin science from molecules to population health · 2026Review
- Home Phototherapy for Psoriasis: Evidence and Implementation.Photodermatology, photoimmunology & photomedicine · 2026Review
- Mitigating hallucinations in synthesized clinical texts to improve multimodal deep learning for dermatology.Journal of biomedical informatics · 2026Article
- Skin Cancer in the Rural South: Strengthening Dermatologic Training for Frontline Primary Care.Cureus · 2026Article
- Implementing GPT-4 Learning Models in Dermatology: An Assessment of Medical Quality and Utility.Skin research and technology : official journal of International Society for Bioengineering and the Skin (ISBS) [and] International Society for Digital Imaging of Skin (ISDIS) [and] International Society for Skin Imaging (ISSI) · 2026Article
- An attention based optimized network for the classification of skin lesions.Scientific reports · 2026Article
- Multi-task AI models in dermatology: Overcoming critical clinical translation challenges for enhanced skin lesion diagnosis.Journal of the European Academy of Dermatology and Venereology : JEADV · 2025Article
- Validation of a Dermatology-Focused Multimodal Large Language Model in Classification of Pigmented Skin Lesions.Diagnostics (Basel, Switzerland) · 2025Article
- Examining Demographic, Geographic, and Temporal Patterns of Melanoma Incidence in Texas From 2000 to 2018: Retrospective Study.JMIR cancer · 2025Article
- Access to dermatologic care: a comparative analysis of primary and satellite clinics.Archives of dermatological research · 2025Article
- Geospatial analysis of dermatologist distribution and access among US seniors.JAAD international · 2025Article
- The Effect of Persistent Poverty on Cancer Outcomes in California.Journal of registry management · 2025Article
- Asynchronous Teledermatology Reduces Wait Times and Maintains High Adherence to Standard Care for Lichen Planus: A Retrospective Study.Telemedicine reports · 2025Article
- Review
- The Impact of Medicaid Expansion on Stage at Diagnosis of Melanoma Patients: A Retrospective Study.Cancers · 2024Article
- Inpatient prevalence and factors associated with Merkel Cell Carcinoma inpatient hospitalization in the United States.Archives of dermatological research · 2024Observational
- Pre-trained multimodal large language model enhances dermatological diagnosis using SkinGPT-4.Nature communications · 2024Article
1 more citing papers are in PubMed but not listed here.
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Importance: As the US population continues to increase and age, there is an unmet need for dermatologic care; therefore, it is important to identify and understand the characteristics and patterns of the dermatologist workforce. Objective: To analyze the longitudinal dermatologist density and urban-rural disparities using a standardized classification scheme. Design, Setting, and Participants: This study analyzed county-level data for 1995 to 2013 from the Area Health Resources File to evaluate the longitudinal trends and demographic and environmental factors associated with the geographic distribution of dermatologists. Main Outcomes and Measures: Active US dermatologist and physician density. Results: In this study of nationwide data on dermatologists, dermatologist density increased by 21% from 3.02 per 100 000 people to 3.65 per 100 000 people from 1995 to 2013; the gap between the density of dermatologists in urban and other areas increased from 2.63 to 3.06 in nonmetropolitan areas and from 3.41 to 4.03 in rural areas. The ratio of dermatologists older than 55 years to younger than 55 years increased 75% in nonmetropolitan and rural areas (from 0.32 to 0.56) and 170% in metropolitan areas (from 0.34 to 0.93). Dermatologists tended to be located in well-resourced, urban communities. Conclusions and Relevance: Our findings suggest that substantial disparities in the geographic distribution of dermatologists exist and have been increasing with time. Correcting the workforce disparity is important for patient care.
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