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Background: Citation screening for scoping searches and rapid review is time-consuming and inefficient, often requiring days or sometimes months to complete. We examined the reliability of PICo-based title-only screening using keyword searches based on the PICo elements-Participants, Interventions, and Comparators, but not the Outcomes., Methods: A convenience sample of 10 datasets, derived from the literature searches of completed systematic reviews, was used to test PICo-based title-only screening. Search terms for screening were generated from the inclusion criteria of each review, specifically the PICo elements-Participants, Interventions and Comparators. Synonyms for the PICo terms were sought, including alternatives for clinical conditions, trade names of generic drugs and abbreviations for clinical conditions, interventions and comparators. The MeSH database, Wikipedia, Google searches and online thesauri were used to assist generating terms. Title-only screening was performed by five reviewers independently in Endnote X7 reference management software using OR Boolean operator. Outcome measures were recall of included studies and the reduction in screening effort. Recall is the proportion of included studies retrieved using PICo title-only screening out of the total number of included studies in the original reviews. The percentage reduction in screening effort is the proportion of records not needing screening because the method eliminates them from the screen set., Results: Across the 10 reviews, the reduction in screening effort ranged from 11 to 78% with a median reduction of 53%. In nine systematic reviews, the recall of included studies was 100%. In one review (oxygen therapy), four of five reviewers missed the same included study (median recall 67%). A post hoc analysis was performed on the dataset with the lowest reduction in screening effort (11%), and it was rescreened using only the intervention and comparator keywords and omitting keywords for participants. The reduction in screening effort increased to 57%, and the recall of included studies was maintained (100%)., Conclusions: In this sample of datasets, PICo-based title-only screening was able to expedite citation screening for scoping searches and rapid reviews by reducing the number of citations needed to screen but requires a thorough workup of the potential synonyms and alternative terms. Further research which evaluates the feasibility of this technique with heterogeneous datasets in different fields would be useful to inform the generalisability of this technique.

Background: Bibliographic databases are the primary resource for identifying systematic reviews of health care interventions. Reliable retrieval of systematic reviews depends on the scope of indexing used by database providers. Therefore, searching one database may be insufficient, but it is unclear how many need to be searched. We sought to evaluate the performance of seven major bibliographic databases for the identification of systematic reviews for hypertension., Methods: We searched seven databases (Cochrane library, Database of Abstracts of Reviews of Effects (DARE), Excerpta Medica Database (EMBASE), Epistemonikos, Medical Literature Analysis and Retrieval System Online (MEDLINE), PubMed Health and Turning Research Into Practice (TRIP)) from 2003 to 2015 for systematic reviews of any intervention for hypertension. Citations retrieved were screened for relevance, coded and checked for screening consistency using a fuzzy text matching query. The performance of each database was assessed by calculating its sensitivity, precision, the number of missed reviews and the number of unique records retrieved., Results: Four hundred systematic reviews were identified for inclusion from 11,381 citations retrieved from seven databases. No single database identified all the retrieved systematic reviews for hypertension. EMBASE identified the most reviews (sensitivity 69 %) but also retrieved the most irrelevant citations with 7.2 % precision (Pr). The sensitivity of the Cochrane library was 60 %, DARE 57 %, MEDLINE 57 %, PubMed Health 53 %, Epistemonikos 49 % and TRIP 33 %. EMBASE contained the highest number of unique records (n = 43). The Cochrane library identified seven unique records and had the highest precision (Pr = 30 %), followed by Epistemonikos (n = 2, Pr = 19 %). No unique records were found in PubMed Health (Pr = 24 %) DARE (Pr = 21 %), TRIP (Pr = 10 %) or MEDLINE (Pr = 10 %). Searching EMBASE and the Cochrane library identified 88 % of all systematic reviews in the reference set, and searching the freely available databases (Cochrane, Epistemonikos, MEDLINE) identified 83 % of all the reviews. The databases were re-analysed after systematic reviews of non-conventional interventions (e.g. yoga, acupuncture) were removed. Similarly, no database identified all the retrieved systematic reviews. EMBASE identified the most relevant systematic reviews (sensitivity 73 %) but also retrieved the most irrelevant citations with Pr = 5 %. The sensitivity of the Cochrane database was 62 %, followed by MEDLINE (60 %), DARE (55 %), PubMed Health (54 %), Epistemonikos (50 %) and TRIP (31 %). The precision of the Cochrane library was the highest (20 %), followed by PubMed Health (Pr = 16 %), DARE (Pr = 13 %), Epistemonikos (Pr = 12 %), MEDLINE (Pr = 6 %), TRIP (Pr = 6 %) and EMBASE (Pr = 5 %). EMBASE contained the most unique records (n = 34). The Cochrane library identified seven unique records. The other databases held no unique records., Conclusions: The coverage of bibliographic databases varies considerably due to differences in their scope and content. Researchers wishing to identify systematic reviews should not rely on one database but search multiple databases.

Background: Citation screening is time consuming and inefficient. We sought to evaluate the performance of Abstrackr, a semi-automated online tool for predictive title and abstract screening., Methods: Four systematic reviews (aHUS, dietary fibre, ECHO, rituximab) were used to evaluate Abstrackr. Citations from electronic searches of biomedical databases were imported into Abstrackr, and titles and abstracts were screened and included or excluded according to the entry criteria. This process was continued until Abstrackr predicted and classified the remaining unscreened citations as relevant or irrelevant. These classification predictions were checked for accuracy against the original review decisions. Sensitivity analyses were performed to assess the effects of including case reports in the aHUS dataset whilst screening and the effects of using larger imbalanced datasets with the ECHO dataset. The performance of Abstrackr was calculated according to the number of relevant studies missed, the workload saving, the false negative rate, and the precision of the algorithm to correctly predict relevant studies for inclusion, i.e. further full text inspection., Results: Of the unscreened citations, Abstrackr's prediction algorithm correctly identified all relevant citations for the rituximab and dietary fibre reviews. However, one relevant citation in both the aHUS and ECHO reviews was incorrectly predicted as not relevant. The workload saving achieved with Abstrackr varied depending on the complexity and size of the reviews (9 % rituximab, 40 % dietary fibre, 67 % aHUS, and 57 % ECHO). The proportion of citations predicted as relevant, and therefore, warranting further full text inspection (i.e. the precision of the prediction) ranged from 16 % (aHUS) to 45 % (rituximab) and was affected by the complexity of the reviews. The false negative rate ranged from 2.4 to 21.7 %. Sensitivity analysis performed on the aHUS dataset increased the precision from 16 to 25 % and increased the workload saving by 10 % but increased the number of relevant studies missed. Sensitivity analysis performed with the larger ECHO dataset increased the workload saving (80 %) but reduced the precision (6.8 %) and increased the number of missed citations., Conclusions: Semi-automated title and abstract screening with Abstrackr has the potential to save time and reduce research waste.

Background: A major problem arising from searching across bibliographic databases is the retrieval of duplicate citations. Removing such duplicates is an essential task to ensure systematic reviewers do not waste time screening the same citation multiple times. Although reference management software use algorithms to remove duplicate records, this is only partially successful and necessitates removing the remaining duplicates manually. This time-consuming task leads to wasted resources. We sought to evaluate the effectiveness of a newly developed deduplication program against EndNote., Methods: A literature search of 1,988 citations was manually inspected and duplicate citations identified and coded to create a benchmark dataset. The Systematic Review Assistant-Deduplication Module (SRA-DM) was iteratively developed and tested using the benchmark dataset and compared with EndNote's default one step auto-deduplication process matching on ('author', 'year', 'title'). The accuracy of deduplication was reported by calculating the sensitivity and specificity. Further validation tests, with three additional benchmarked literature searches comprising a total of 4,563 citations were performed to determine the reliability of the SRA-DM algorithm., Results: The sensitivity (84%) and specificity (100%) of the SRA-DM was superior to EndNote (sensitivity 51%, specificity 99.83%). Validation testing on three additional biomedical literature searches demonstrated that SRA-DM consistently achieved higher sensitivity than EndNote (90% vs 63%), (84% vs 73%) and (84% vs 64%). Furthermore, the specificity of SRA-DM was 100%, whereas the specificity of EndNote was imperfect (average 99.75%) with some unique records wrongly assigned as duplicates. Overall, there was a 42.86% increase in the number of duplicates records detected with SRA-DM compared with EndNote auto-deduplication., Conclusions: The Systematic Review Assistant-Deduplication Module offers users a reliable program to remove duplicate records with greater sensitivity and specificity than EndNote. This application will save researchers and information specialists time and avoid research waste. The deduplication program is freely available online.