ArticleArchives of dermatological research2021
Reliable test of clinicians' mastery in skin cancer diagnostics.
Article in Archives of dermatological research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to 2 registered trials, which are not on this map. Cited by 4 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Correlation Between Online Case-based Training of Pathologists in Dermoscopic Images and Diagnostic Accuracy in Histopathological Interpretation of Skin Lesions Suspicious of Melanoma
Diagnostic Agreement Among Expert Dermatopathologists on Skin Lesions Clinically Suspicious of Melanoma
Who cites it
4 citing papers in PubMed.
- The impact of repeated item development training on the prediction of medical faculty members' item difficulty index.BMC medical education · 2024Article
- Improving Skin Cancer Diagnostics Through a Mobile App With a Large Interactive Image Repository: Randomized Controlled Trial.JMIR dermatology · 2023Article
- Theory-Based Approaches to Support Dermoscopic Image Interpretation Education: A Review of the Literature.Dermatology practical & conceptual · 2022Review
- Implementation and Impact of a Dermoscopy-Enabled Dermatology eConsult Application in Primary Care.Journal of primary care & community healthArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
Abstract
Differentiating between benign and malignant skin lesions can be very difficult and should only be done by sufficiently trained and skilled clinicians. To our knowledge there are no validated tests for reliable assessments of clinicians' ability to perform skin cancer diagnostics. To develop and gather validity evidence for a test in skin cancer diagnostics, a multiple-choice questionnaire (MCQ) was developed based on informal interviews with seven content experts from five skin cancer centers in Denmark. Validity evidence for the test was gathered from May until July 2019 using Messick's validity framework (content, response process, internal structure, relationship to other variables and consequences). Item content was revised through a Delphi-like review process and then piloted on 36 medical students and 136 doctors using a standardized response process. Results enabled an analysis of the internal structure and relationship to other variables of the test. Finally, the contrasting groups method was used to investigate the test's consequences (pass-fail standard). The initial 90-item MCQ was reduced to 40 items during the Delphi-like review process. Item analysis revealed that 25 of the 40 selected items were level I-III quality items with a high internal consistency (Cronbach's α = 0.83) and highly significant (P ≤ 0.0001) differences in test scores between participants with different occupations or levels of experience. A pass-fail standard of 12 (48%) correct answers was established using the contrasting groups' method. The skin cancer diagnostics MCQ developed in this study can be used for reliable assessments of clinicians' competencies.
Indexed as
Identifiers
32596742What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.