https://socictopen.socict.org/files/original/96fe4d8e2443aef8f5b7fb64e94d5c63.pdf ca375e7c153361b736e6300a6a3bbc5d Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Coronavirus Description An account of the resource Dominio científico: Coronavirus Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource When predictions are used to allocate scarce health care resources: three considerations for models in the era of Covid-19 Creator An entity primarily responsible for making the resource Richard R. Sharp, David M. Kent, Jessica K. Paulus, Negin Hajizadeh Description An account of the resource Abstract Background The need for life-saving interventions such as mechanical ventilation may threaten to outstrip resources during the Covid-19 pandemic. Allocation of these resources to those most likely to benefit can be supported by clinical prediction models. The ethical and practical considerations relevant to predictions supporting decisions about microallocation are distinct from those that inform shared decision-making in ways important for model design. Main body We review three issues of importance for microallocation: (1) Prediction of benefit (or of medical futility) may be technically very challenging; (2) When resources are scarce, calibration is less important for microallocation than is ranking to prioritize patients, since capacity determines thresholds for resource utilization; (3) The concept of group fairness, which is not germane in shared decision-making, is of central importance in microallocation. Therefore, model transparency is important. Conclusion Prediction supporting allocation of life-saving interventions should be explicit, data-driven, frequently updated and open to public scrutiny. This implies a preference for simple, easily understood and easily applied prognostic models. Date A point or period of time associated with an event in the lifecycle of the resource 2020 Subject The topic of the resource health care rationing, algorithmic fairness, COVID-19, Clinical prediction models Identifier An unambiguous reference to the resource within a given context DOI: 10.1186/s41512-020-00079-y Source A related resource from which the described resource is derived Diagnostic and Prognostic Research Publisher An entity responsible for making the resource available BMC Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Medicine (General)