Dataset Information

Considerations for conducting systematic reviews: evaluating the performance of different methods for de-duplicating references.

ABSTRACT:

Background

Systematic reviews involve searching multiple bibliographic databases to identify eligible studies. As this type of evidence synthesis is increasingly pursued, the use of various electronic platforms can help researchers improve the efficiency and quality of their research. We examined the accuracy and efficiency of commonly used electronic methods for flagging and removing duplicate references during this process.

Methods

A heterogeneous sample of references was obtained by conducting a similar topical search in MEDLINE, Embase, Cochrane Central Register of Controlled Trials, and PsycINFO databases. References were de-duplicated via manual abstraction to create a benchmark set. The default settings were then used in Ovid multifile search, EndNote desktop, Mendeley, Zotero, Covidence, and Rayyan to de-duplicate the sample of references independently. Using the benchmark set as reference, the number of false-negative and false-positive duplicate references for each method was identified, and accuracy, sensitivity, and specificity were determined.

Results

We found that the most accurate methods for identifying duplicate references were Ovid, Covidence, and Rayyan. Ovid and Covidence possessed the highest specificity for identifying duplicate references, while Rayyan demonstrated the highest sensitivity.

Conclusion

This study reveals the strengths and weaknesses of commonly used de-duplication methods and provides strategies for improving their performance to avoid unintentionally removing eligible studies and introducing bias into systematic reviews. Along with availability, ease-of-use, functionality, and capability, these findings are important to consider when researchers are selecting database platforms and supporting software programs for conducting systematic reviews.

SUBMITTER: McKeown S

PROVIDER: S-EPMC7827976 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Considerations for conducting systematic reviews: evaluating the performance of different methods for de-duplicating references.

McKeown Sandra S Mir Zuhaib M ZM

Systematic reviews 20210123 1

<h4>Background</h4>Systematic reviews involve searching multiple bibliographic databases to identify eligible studies. As this type of evidence synthesis is increasingly pursued, the use of various electronic platforms can help researchers improve the efficiency and quality of their research. We examined the accuracy and efficiency of commonly used electronic methods for flagging and removing duplicate references during this process.<h4>Methods</h4>A heterogeneous sample of references was obtain ...[more]

PMID: 33485394

Similar Datasets

Project description:Venom-gland transcriptomics is a key tool in the study of the evolution, ecology, function, and pharmacology of animal venoms. In particular, gene-expression variation and coding sequences gained through transcriptomics provide key information for explaining functional venom variation over both ecological and evolutionary timescales. The accuracy and usefulness of inferences made through transcriptomics, however, is limited by the accuracy of the transcriptome assembly, which is a bioinformatic problem with several possible solutions. Several methods have been employed to assemble venom-gland transcriptomes, with the Trinity assembler being the most commonly applied among them. Although previous evidence of variation in performance among assembly software exists, particularly regarding recovery of difficult-to-assemble multigene families such as snake venom metalloproteinases, much work to date still employs a single assembly method. We evaluated the performance of several commonly used de novo assembly methods for the recovery of both nontoxin transcripts and complete, high-quality venom-gene transcripts across eleven snake and four scorpion transcriptomes. We varied k-mer sizes used by some assemblers to evaluate the impact of k-mer length on transcript recovery. We showed that the recovery of nontoxin transcripts and toxin transcripts is best accomplished through different assembly software, with SDT at smaller k-mer lengths and Trinity being best for nontoxin recovery and a combination of SeqMan NGen and a seed-and-extend approach implemented in Extender as the best means of recovering a complete set of toxin transcripts. In particular, Extender was the only means tested capable of assembling multiple isoforms of the diverse snake venom metalloproteinase family, while traditional approaches such as Trinity recovered at most one metalloproteinase transcript. Our work demonstrated that traditional metrics of assembly performance are not predictive of performance in the recovery of complete and high quality toxin genes. Instead, effective venom-gland transcriptomic studies should combine and quality-filter the results of several assemblers with varying algorithmic strategies.

Project description:BackgroundNLP models like ChatGPT promise to revolutionize text-based content delivery, particularly in medicine. Yet, doubts remain about ChatGPT's ability to reliably support evaluations of cognitive performance, warranting further investigation into its accuracy and comprehensiveness in this area.MethodA cohort of 60 cognitively normal individuals and 30 stroke survivors underwent a comprehensive evaluation, covering memory, numerical processing, verbal fluency, and abstract thinking. Healthcare professionals and NLP models GPT-3.5 and GPT-4 conducted evaluations following established standards. Scores were compared, and efforts were made to refine scoring protocols and interaction methods to enhance ChatGPT's potential in these evaluations.ResultWithin the cohort of healthy participants, the utilization of GPT-3.5 revealed significant disparities in memory evaluation compared to both physician-led assessments and those conducted utilizing GPT-4 (P < 0.001). Furthermore, within the domain of memory evaluation, GPT-3.5 exhibited discrepancies in 8 out of 21 specific measures when compared to assessments conducted by physicians (P < 0.05). Additionally, GPT-3.5 demonstrated statistically significant deviations from physician assessments in speech evaluation (P = 0.009). Among participants with a history of stroke, GPT-3.5 exhibited differences solely in verbal assessment compared to physician-led evaluations (P = 0.002). Notably, through the implementation of optimized scoring methodologies and refinement of interaction protocols, partial mitigation of these disparities was achieved.ConclusionChatGPT can produce evaluation outcomes comparable to traditional methods. Despite differences from physician evaluations, refinement of scoring algorithms and interaction protocols has improved alignment. ChatGPT performs well even in populations with specific conditions like stroke, suggesting its versatility. GPT-4 yields results closer to physician ratings, indicating potential for further enhancement. These findings highlight ChatGPT's importance as a supplementary tool, offering new avenues for information gathering in medical fields and guiding its ongoing development and application.

Project description:BackgroundPolicy makers and others often require synthesis of knowledge in an area within six months or less. Traditional systematic reviews typically take at least 12 months to conduct. Rapid reviews streamline traditional systematic review methods in order to synthesize evidence within a shortened timeframe. There is great variation in the process of conducting rapid reviews. This review sought to examine methods used for rapid reviews, as well as implications of methodological streamlining in terms of rigour, bias, and results.MethodsA comprehensive search strategy--including five electronic databases, grey literature, hand searching of relevant journals, and contacting key informants--was undertaken. All titles and abstracts (n = 1,989) were reviewed independently by two reviewers. Relevance criteria included articles published between 1995 and 2009 about conducting rapid reviews or addressing comparisons of rapid reviews versus traditional reviews. Full articles were retrieved for any titles deemed relevant by either reviewer (n = 70). Data were extracted from all relevant methodological articles (n = 45) and from exemplars of rapid review methods (n = 25).ResultsRapid reviews varied from three weeks to six months; various methods for speeding up the process were employed. Some limited searching by years, databases, language, and sources beyond electronic searches. Several employed one reviewer for title and abstract reviewing, full text review, methodological quality assessment, and/or data extraction phases. Within rapid review studies, accelerating the data extraction process may lead to missing some relevant information. Biases may be introduced due to shortened timeframes for literature searching, article retrieval, and appraisal.ConclusionsThis review examined the continuum between diverse rapid review methods and traditional systematic reviews. It also examines potential implications of streamlined review methods. More of these rapid reviews need to be published in the peer-reviewed literature with an emphasis on articulating methods employed. While one consistent methodological approach may not be optimal or appropriate, it is important that researchers undertaking reviews within the rapid to systematic continuum provide detailed descriptions of methods used and discuss the implications of their chosen methods in terms of potential bias introduced. Further research comparing full systematic reviews with rapid reviews will enhance understanding of the limitations of these methods.

Dataset Information

Considerations for conducting systematic reviews: evaluating the performance of different methods for de-duplicating references.

Background

Methods

Results

Conclusion

Publications

Considerations for conducting systematic reviews: evaluating the performance of different methods for de-duplicating references.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets