An overview of recommendations and translational milestones for genomic tests in cancer is a research paper published in Genetics in Medicine (2015). On theSindex it has a DataRank of 0.475. It has been cited 8 times, with 6 citing works in its 1-hop citation network.
PurposeTo understand the translational trajectory of genomic tests in cancer screening, diagnosis, prognosis, and treatment, we reviewed tests that have been assessed by recommendation and guideline developers.MethodsFor each test, we marked translational milestones by determining when the genomic association with cancer was first discovered and studied in patients, and when a health application for a specified clinical use was successfully demonstrated and approved or cleared by the US Food and Drug Administration. To identify recommendations and guidelines, we reviewed the websites of cancer, genomic, and general guideline developers and professional organizations. We searched the in vitro diagnostics database of the US Food and Drug Administration for information, and we searched PubMed for translational milestones. Milestones were examined against type of recommendation, Food and Drug Administration approval or clearance, disease rarity, and test purpose.ResultsOf the 45 tests we identified, 9 received strong recommendations for their usage in clinical settings, 14 received positive but moderate recommendations, and 22 were not currently recommended. For 18 tests, two or more different sources had issued recommendations, with 67% concordance. Only five tests had Food and Drug Administration approval, and an additional five had clearance. The median time from discovery to recommendation statement was 14.7 years.ConclusionIn general, there were no associations found between translational trajectory and recommendation category.Genet Med 17 6, 431-440.
FAIR checklist signals are shown for context only and do not affect DataRank scoring.
Base Score Contribution
0.330
From this paper's citation signal
Citation Network Contribution
0.145
From 6 citing papers with measurable signal
Ranked by citation count — the same ordering the engine uses when summing log1p(Cq) over citers.
DataRank blends this paper's own citation count with the influence of the papers that cite it. Here, roughly 69% comes from its base citations and 31% from the citation network (6 citing papers contributed measurable signal).
Citers are pulled from OpenAlex sorted by cited_by_count:descand capped per paper, so when the cap binds we keep the highest-signal references and the score is reproducible across reruns.
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