The MMSE contributes to a diagnosis of dementia in low prevalence settings, but should not be used in isolation to confirm or exclude disease. We recommend that future work evaluates the diagnostic accuracy of tests in the context of the diagnostic pathway experienced by the patient and that investigators report how undergoing the MMSE changes patient-relevant outcomes.

Background Information specialists frequently translate search filters from one interface to another. Publications advise that translation can be complex and should be undertaken carefully. Objectives To investigate the issues arising when translating the Cochrane Embase RCT search filter from one interface (Ovid) to another (http://Embase.com). Methods We drafted a translation of the Cochrane Ovid RCT filter to run in http://Embase.com. We compared the line‐by‐line results of the Ovid filter with the results of the translation. We revised the filter. We identified differences between database versions including records with different publication years and subject headings. Some records were in Embase in one interface but not in the other. We encountered expected interface differences relating to proximity operators. We also encountered unexpected interface issues around truncation and the use of the original title or original abstract field. Discussion Filter conversion is challenging and time consuming revealing unexpected differences in interfaces and databases. Careful planning can pre‐empt some issues, but others may only emerge during testing. We identified interface anomalies that have led database publishers to review aspects of the way their interfaces work. Conclusions Translators should be vigilant for known and unknown differences in both interfaces and database versions.

Background Evidence synthesis reviews in health care rely on the efficient identification of research evidence, particularly evidence from randomised controlled trials (RCTs). There are no recently validated filters to identify RCTs in the Cumulative Index to Nursing and Allied Health Literature (CINAHL Plus). Objectives To develop, test and validate a search filter to identify reports of RCTs from CINAHL Plus. Methods Nine sets of relevant and irrelevant records were identified to develop and test search filters iteratively. Two sets were used to validate the sensitivity and precision of the filters. The performance of two previously published filters and the filter built into EBSCOhost was evaluated. Results We present a validated filter which offers sensitivity of 0.88 (95% CI: 0.77–0.95) and precision of 0.36 (95% CI: 0.31–0.41). This is comparable to the sensitivity of published filters, but has much better precision. Conclusions A sensitive and precise filter, developed using records selected based on title and abstract information, is available for identifying reports of RCTs in the CINAHL Plus database via EBSCOhost. Using this filter is likely to reduce the number of results needing to be screened to a quarter of those retrieved by other published filters.

Background and objectives: The Cochrane Central Register of Controlled Trials (CENTRAL) is compiled from a number of sources, including PubMed and Embase. Since 2017, we have increased the number of sources feeding into CENTRAL and improved the efficiency of our processes through the use of application programming interfaces, machine learning, and crowdsourcing.Our objectives were twofold: (1) Assess the effectiveness of Cochrane's centralized search and screening processes to correctly identify references to published reports which are eligible for inclusion in Cochrane systematic reviews of randomized controlled trials (RCTs). (2) Identify opportunities to improve the performance of Cochrane's centralized search and screening processes to identify references to eligible trials. Methods: We identified all references to RCTs (either published journal articles or trial registration records) with a publication or registration date between 1st January 2017 and 31st December 2018 that had been included in a Cochrane intervention review. We then viewed an audit trail for each included reference to determine if it had been identified by our centralized search process and subsequently added to CENTRAL. Results: We identified 650 references to included studies with a publication year of 2017 or 2018. Of those, 634 (97.5%) had been captured by Cochrane's Centralised Search Service. Sixteen references had been missed by the Cochrane's Centralised Search Service: six had PubMed-not-MEDLINE status, four were missed by the centralized Embase search, three had been misclassified by Cochrane Crowd, one was from a journal not indexed in MEDLINE or Embase, one had only been added to Embase in 2019, and one reference had been rejected by the automated RCT machine learning classifier. Of the sixteen missed references, eight were the main or only publication to the trial in the review in which it had been included. Conclusion: This analysis has shown that Cochrane's centralized search and screening processes are highly sensitive. It has also helped us to understand better why some references to eligible RCTs have been missed. The CSS is playing a critical role in helping to populate CENTRAL and is moving us toward making CENTRAL a comprehensive repository of RCTs.