Any intervention that utilized a pharmacist to improve osteoporosis management was eligible. Manual searches of reference lists from eligible studies and a grey literature search were also completed [7, 8]. Our grey literature search targeted government, Tozasertib chemical structure research institution, professional association, and osteoporosis foundation websites to try to capture research published as a report and not accessible through traditional research

databases, Appendix Table 5. Abstracts, commentaries, letters, news articles, and review papers were excluded. Titles and abstracts were reviewed for relevance by two authors (MNE, AMB), and discrepancies were settled through consultation with a third author (SMC). All relevant publications were identified, yet only RCTs were eligible for detailed review. We therefore Bucladesine identified all papers that included a pharmacist in the context of osteoporosis management, yet focused on RCTs as these may provide

the highest quality of evidence [8]. RCT data abstraction Study characteristics including research design, setting, pharmacist training, patient inclusion criteria, patient recruitment, intervention details, and outcomes were abstracted by two authors (MNE, AMB) and confirmed by a third author (SMC). Since the ultimate goal of identifying high-risk patients is treatment to reduce fracture risk, our a priori focus was on process of care outcomes related to improved identification of at-risk individuals (e.g., BMD testing and physician follow-up) and osteoporosis treatment initiation. We had intended to examine the impact of pharmacist Caspase Inhibitor VI mouse interventions on osteoporosis treatment adherence;

however, no relevant study was identified. After the identification of relevant literature, we decided to summarize information concerning improvements in calcium and vitamin D intake or supplementation. Qualitative assessment of risk of bias We qualitatively examined the threats to internal validity for each trial based on risk for allocation bias, attrition bias, detection bias, and performance bias [8, 9]. Following recent guidelines to improve terminology in non-experimental research [10], we grouped these four potential biases into two types: (1) selection bias, related to allocation and attrition, SPTBN5 and (2) information bias, related to detection and performance. Allocation bias occurs when randomization fails such that comparison groups differ on important prognostic variables. Attrition bias occurs when patients who continue to be followed are systematically different from those who are lost to follow-up in ways that impact outcomes. Detection and performance biases are classified as different types of information bias—biases that occur when there are systematic differences in the completeness or accuracy of data that lead to differential misclassification of patient characteristics, exposure, or outcomes [10].