{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,16]],"date-time":"2026-09-16T16:24:38Z","timestamp":1789575878201,"version":"build-2803163510"},"reference-count":52,"publisher":"SAGE Publications","issue":"10","license":[{"start":{"date-parts":[[2021,9,30]],"date-time":"2021-09-30T00:00:00Z","timestamp":1632960000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Diabetes Technology &amp; Therapeutics"],"published-print":{"date-parts":[[2021,10]]},"abstract":"<jats:p>\n                    Numerous methods have been proposed as measures of quality of glycemic control resulting in confusion regarding the best choice of metric to use by clinicians and researchers. Some methods use a single metric such as\n                    <jats:bold>HbA1c<\/jats:bold>\n                    ,\n                    <jats:bold>Mean Glucose<\/jats:bold>\n                    , %\n                    <jats:bold>T<\/jats:bold>\n                    ime\n                    <jats:bold>I<\/jats:bold>\n                    n\n                    <jats:bold>R<\/jats:bold>\n                    ange (\n                    <jats:bold>\n                      <jats:italic toggle=\"yes\">%TIR<\/jats:italic>\n                    <\/jats:bold>\n                    ), or Coefficient of Variation (\n                    <jats:bold>%CV<\/jats:bold>\n                    ). Others use a combination of up to seven metrics, for example, Q-Score, Comprehensive Glucose Pentagon (\n                    <jats:bold>CGP<\/jats:bold>\n                    ), and Personal Glycemic State (\n                    <jats:bold>PGS<\/jats:bold>\n                    ). A recently proposed\n                    <jats:bold>Co<\/jats:bold>\n                    mposite continuous\n                    <jats:bold>G<\/jats:bold>\n                    lucose monitoring\n                    <jats:bold>i<\/jats:bold>\n                    ndex utilizes three metrics:\n                    <jats:bold>\n                      <jats:italic toggle=\"yes\">%TIR<\/jats:italic>\n                    <\/jats:bold>\n                    ,\n                    <jats:bold>T<\/jats:bold>\n                    ime\n                    <jats:bold>B<\/jats:bold>\n                    elow\n                    <jats:bold>R<\/jats:bold>\n                    ange (\n                    <jats:bold>\n                      <jats:italic toggle=\"yes\">%TBR<\/jats:italic>\n                    <\/jats:bold>\n                    ), and standard deviation (\n                    <jats:bold>\n                      <jats:italic toggle=\"yes\">SD<\/jats:italic>\n                    <\/jats:bold>\n                    ) of glucose. This review proposes that only two metrics can be sufficient when monitoring an individual patient or when comparing two or more forms of management interventions. These two metrics comprise (1) a measure of\n                    <jats:italic toggle=\"yes\">efficacy<\/jats:italic>\n                    such as\n                    <jats:bold>Mean Glucose<\/jats:bold>\n                    ,\n                    <jats:bold>HbA1c<\/jats:bold>\n                    ,\n                    <jats:bold>\n                      <jats:italic toggle=\"yes\">\n                        <jats:sc>%TIR<\/jats:sc>\n                      <\/jats:italic>\n                    <\/jats:bold>\n                    , or %Time Above Range (\n                    <jats:bold>\n                      <jats:italic toggle=\"yes\">%TAR<\/jats:italic>\n                    <\/jats:bold>\n                    ) and (2) a measure of\n                    <jats:italic toggle=\"yes\">safety<\/jats:italic>\n                    based on risk of hypoglycemia such as\n                    <jats:bold>\n                      <jats:italic toggle=\"yes\">%TBR<\/jats:italic>\n                    <\/jats:bold>\n                    ,\n                    <jats:bold>L<\/jats:bold>\n                    ow Blood\n                    <jats:bold>G<\/jats:bold>\n                    lucose\n                    <jats:bold>I<\/jats:bold>\n                    ndex (\n                    <jats:bold>L<\/jats:bold>\n                    B\n                    <jats:bold>GI)<\/jats:bold>\n                    , or frequency of specified types of hypoglycemic events per patient year. By analysis of the two-dimensional graphical and statistical relationships between metrics for\n                    <jats:bold>safety<\/jats:bold>\n                    and\n                    <jats:bold>efficacy<\/jats:bold>\n                    and by testing identity versus nonidentity of these relationships, one can improve sensitivity for detection of the effects of medications and of other therapeutic interventions, avoid the need for arbitrary scoring systems for glucose values falling within versus outside the target range, and offer the advantage of conceptual and practical simplicity.\n                  <\/jats:p>","DOI":"10.1089\/dia.2021.0115","type":"journal-article","created":{"date-parts":[[2021,6,4]],"date-time":"2021-06-04T12:42:56Z","timestamp":1622810576000},"page":"692-704","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":18,"title":["Quality of Glycemic Control: Assessment Using Relationships Between Metrics for Safety and Efficacy"],"prefix":"10.1177","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5547-3564","authenticated-orcid":false,"given":"David","family":"Rodbard","sequence":"first","affiliation":[{"name":"Biomedical Informatics Consultants LLC, Clinical Biostatistics Department, Potomac, Maryland, USA."}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2021,9,30]]},"reference":[{"key":"e_1_3_3_2_2","unstructured":"Rodbard D Berger M Pernick N: Computer networking and information systems to facilitate delivery of health care to patients with diabetes. 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