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Alper, Brian S.

https://orcid.org/0000-0003-4300-4928

Mayo Clinic

Computable Publishing

Citations 3

GRADE Certainty Ratings: Thresholds Rather Than Categories of Contextualization (GRADE Guidance 41)
Hultcrantz, Monica , Schünemann, Holger J. , Rind, David M., Tovey, David , Alper, Brian S. , Akl, Elie A. , Saif‐Ur‐Rahman, KM , Sousa‐Pinto, Bernardo , Mustafa, Reem A , Mayer, Martin , Tovey, David , Alper, Brian S , Akl, Elie A , Saif-Ur-Rahman, K M , Schünemann, Holger J , Murad, M. Hassan , Neumann, Ignacio , Izcovich, Ariel , Guyatt, Gordon

Annals of Internal Medicine | June 30, 2025 | Volume: 178 | Issue: 8 | Pages: 1183-1186

10.7326/annals-25-00548 Get It

Categorizing metadata to help mobilize computable biomedical knowledge
Alper, Brian S. , Flynn, Allen , Conte, Marisa , Eldredge, Christina , Gold, Sigfried , Greenes, Robert A. , Haug, Peter J. , Jacoby, Kim, Koru, Güneş , McClay, James C. , Sainvil, Marc L. , Sottara, Davide , Tuttle, Mark S. , Visweswaran, Shyam , Yurk, Robin , Conte, Marisa L , Gold, Sigfried , Koru, Gunes , McClay, James , Sainvil, Marc L , Tuttle, Mark , Visweswaran, Shyam , Yurk, Robin Ann , Alper, Brian S , Bray, Bruce E.

Learning Health Systems | May 10, 2021 | Volume: 6 | Issue: 1 | Pages: No page available

10.1002/lrh2.10271 Get It

AbstractIntroductionComputable biomedical knowledge artifacts (CBKs) are digital objects conveying biomedical knowledge in machine‐interpretable structures. As more CBKs are produced and their complexity increases, the value obtained from sharing CBKs grows. Mobilizing CBKs and sharing them widely can only be achieved if the CBKs are findable, accessible, interoperable, reusable, and trustable (FAIR+T). To help mobilize CBKs, we describe our efforts to outline metadata categories to make CBKs FAIR+T.MethodsWe examined the literature regarding metadata with the potential to make digital artifacts FAIR+T. We also examined metadata available online today for actual CBKs of 12 different types. With iterative refinement, we came to a consensus on key categories of metadata that, when taken together, can make CBKs FAIR+T. We use subject‐predicate‐object triples to more clearly differentiate metadata categories.ResultsWe defined 13 categories of CBK metadata most relevant to making CBKs FAIR+T. Eleven of these categories (type, domain, purpose, identification, location, CBK‐to‐CBK relationships, technical, authorization and rights management, provenance, evidential basis, and evidence from use metadata) are evident today where CBKs are stored online. Two additional categories (preservation and integrity metadata) were not evident in our examples. We provide a research agenda to guide further study and development of these and other metadata categories.ConclusionA wide variety of metadata elements in various categories is needed to make CBKs FAIR+T. More work is needed to develop a common framework for CBK metadata that can make CBKs FAIR+T for all stakeholders.
Defining certainty of net benefit: a GRADE concept paper
Alper, Brian S. , Oettgen, Peter, Kunnamo, Ilkka , Iorio, Alfonso , Ansari, Mohammed Toseef, Murad, M. Hassan , Meerpohl, Joerg J , Hultcrantz, Monica , Schünemann, Holger J. , Iorio, Alfonso , Murad, M Hassan , Meerpohl, Joerg J , Schünemann, Holger J , Ansari, Mohammed Toseef, Qaseem, Amir , Guyatt, Gordon , Alper, Brian S

BMJ Open | June 5, 2019 | Volume: 9 | Issue: 6 | Pages: e027445

10.1136/bmjopen-2018-027445 Get It

Grading of Recommendations Assessment, Development and Evaluation (GRADE) methodology is used to assess and report certainty of evidence and strength of recommendations. This GRADE concept article is not GRADE guidance but introduces certainty of net benefit, defined as the certainty that the balance between desirable and undesirable health effects is favourable. Determining certainty of net benefit requires considering certainty of effect estimates, the expected importance of outcomes and variability in importance, and the interaction of these concepts. Certainty of net harm is the certainty that the net effect is unfavourable. Guideline panels using or testing this approach might limit strong recommendations to actions with a high certainty of net benefit or against actions with a moderate or high certainty of net harm. Recommendations may differ in direction or strength from that suggested by the certainty of net benefit or harm when influenced by cost, equity, acceptability or feasibility.