ReviewMelanoma management2015
Conceptual approach to early melanoma detection: models, tools, issues and challenges.
Review in Melanoma management, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
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0 citing papers in PubMed.
No citing paper in PubMed yet.
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Identification and removal of melanoma early in its development remains the most effective treatment. However, identification of early melanoma remains challenging and may result in unnecessary morbidity due to the excess excision of benign melanocytic nevi. Herein, we present a conceptual model of benign and malignant melanocytic growths. The potential differences in the location of the cell of origin as well as considerations for neoplasm progression are also reviewed. Several of the clinical tools currently available, the integration of information from those different sources, and approaches to set an optimum biopsy threshold are discussed. While early detection remains a challenge, significant progress has been made. Insight into melanoma growth processes and appropriate use of available tools can result in the detection of thinner melanomas while also decreasing overall biopsy rates.
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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.