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Record W4405485932 · doi:10.2196/63861

The Prevalence of Dermoscopy Use Among Dermatology Residents in Riyadh, Saudi Arabia: Cross-Sectional Study

2024· article· en· W4405485932 on OpenAlexvenueno aff
Abdullah Almeziny, Rahaf Almutairi, Amal Altamimi, Khloud Alshehri, Latifah Almehaideb, Asem Shadid, Mohammed Al Mashali

Bibliographic record

VenueJMIR Dermatology · 2024
Typearticle
Languageen
FieldMedicine
TopicCutaneous Melanoma Detection and Management
Canadian institutionsnot available
Fundersnot available
KeywordsPreprintDermatologyMedicineOptometry

Abstract

fetched live from OpenAlex

Background: Dermoscopy is a noninvasive technology used to examine the skin's invisible microstructures in dermatological practice and is gaining prominence as a crucial tool. Dermoscopy is an evidence-based practice used to enhance the early detection of skin malignancies and to help distinguish between various skin conditions, including pigmented and nonpigmented skin malignancies. Currently, the vast majority of global guidelines for skin cancer recommend dermoscopy as a critical component. Dermoscopy use is increasing worldwide, but to date, no study has documented the attitudes toward and use of dermoscopy among future dermatologists in Saudi Arabia. Objective: We aimed to determine the proportion of dermatology residents in Riyadh who use dermoscopy in their clinical practice; identify factors influencing the use of dermoscopy, such as availability of equipment, training, and the perceived importance of dermoscopy in clinical practice; explore barriers to dermoscopy use, including the lack of access to necessary resources (eg, dermoscopes) and insufficient training; and provide insights into the adoption and integration of dermoscopy into dermatology training and clinical practice in Saudi Arabia. Methods: In January 2024, a validated and published questionnaire was modified to meet research requirements and was sent to all registered dermatology residents in the The Saudi Board of Dermatology and Venereology Program. Results: In total, 63 dermatology residents in Riyadh, Saudi Arabia, completed the web-based questionnaire (response rate=87.5%). The sample was predominantly female (n=34, 54.0%), with the majority (n=53, 84.1%) aged between 26 and 30 years. A notable proportion of participants (n=22, 34.9%) were in their final year of residency. Over half of the participants (n=34, 54.0%) owned a dermoscope, and a substantial number of them (n=23, 36.5%) reported conducting 21-30 clinic consultations per month on average. More than half of the participants (n=36, 57.1%) had received dermoscopy training, and 16 (36.4%) had used dermoscopy for 2 years. Additionally, most participants (n=20, 45.5%) had used nonpolarized immersion-contact dermoscopy, while 19 (43.2%) had used polarized light dermoscopy. Furthermore, the majority (n=22, 50.0%) used dermoscopy in fewer than 10% of cases involving patients with inflammatory skin lesions. Statistical analysis revealed significant associations between the participants' ages (P=.003), residency levels (P=.001), and practice centers and the use of dermoscopy (P=.004). Conclusions: Dermoscopy has been widely adopted by dermatology residents in their daily clinical practice due to its benefits in early detection and diagnosis of skin diseases. However, the overall extent of dermoscopy use within the dermatology community remains unclear, highlighting the need for further education. In Saudi Arabia, the key factors influencing dermoscopy use include residents' ages, residency levels, and practice centers. Younger dermatologists have expressed strong interest in improving their dermoscopy knowledge and skills. Expanding access to dermoscopy equipment and providing training during residency could further promote its use across the country.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.605

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.021
GPT teacher head0.329
Teacher spread0.308 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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