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Deduplicating records in systematic reviews: there are free, accurate automated ways to do so.
Guimarães, Nathalia Sernizon; Ferreira, Andrêa J F; Ribeiro Silva, Rita de Cássia; de Paula, Adelzon Assis; Lisboa, Cinthia Soares; Magno, Laio; Ichiara, Maria Yury; Barreto, Maurício Lima.
Afiliación
  • Guimarães NS; Institute of Collective Health. Federal University of Bahia, Salvador, Bahia, Brazil. Electronic address: nasernizon@gmail.com.
  • Ferreira AJF; Centre for Data and Knowledge Integration for Health (CIDACS), Oswaldo Cruz Foundation, Salvador, Bahia, Brazil; The Ubuntu Center on Racism, Global Movements, and Population Health Equity, Dornsife School of Public Health, Drexel University, Philadelphia, PA, USA.
  • Ribeiro Silva RC; Centre for Data and Knowledge Integration for Health (CIDACS), Oswaldo Cruz Foundation, Salvador, Bahia, Brazil; Department of Nutrition, School of Nutrition, Federal University of Bahia, Salvador, Bahia, Brazil.
  • de Paula AA; Institute of Collective Health. Federal University of Bahia, Salvador, Bahia, Brazil.
  • Lisboa CS; Pos-graduation programme of Collective Health, State University of Feira de Santana, Feira de Santana, Bahia, Brazil.
  • Magno L; Institute of Collective Health. Federal University of Bahia, Salvador, Bahia, Brazil; Department of Life Sciences, State University of Bahia, Salvador, Bahia, Brazil.
  • Ichiara MY; Centre for Data and Knowledge Integration for Health (CIDACS), Oswaldo Cruz Foundation, Salvador, Bahia, Brazil.
  • Barreto ML; Institute of Collective Health. Federal University of Bahia, Salvador, Bahia, Brazil; Centre for Data and Knowledge Integration for Health (CIDACS), Oswaldo Cruz Foundation, Salvador, Bahia, Brazil.
J Clin Epidemiol ; 152: 110-115, 2022 Dec.
Article en En | MEDLINE | ID: mdl-36241035
ABSTRACT

OBJECTIVE:

Here, we examined the accuracy measures of a set of automated deduplication tools to identify duplicate in the eligibility process of systematic reviews. STUDY DESIGN AND

SETTING:

A planned search strategy was carried out on seven electronic databases until May 31, 2021. Using manual search as the reference standard, we assessed sensibility, specificity, negative predictive value, and positive predictive value (PPV).

RESULTS:

Specificity ranged from 0.96 to 1.00. Rayyan, Mendeley, and Systematic Review Accelerator (SRA) presented high sensibility (0.98 [95% CI = 0.94-1.00]; 0.93 [95% CI = 0.88-0.97] and 0.90 [95% CI = 0.84-0.95], respectively), whereas EndNote X9 and Zotero had only fair sensitivity (0.73 [95% CI = 0.65-0.80] and 0.74 [95% CI = 0.66-0.81], respectively). Negative predictive value ranged from 0.99 to 1.00. Mendeley and SRA had good PPV (0.93 [95% CI = 0.88-0.97] and 0.99 [95% CI = 0.96-1.00], respectively). PPV was fair for EndNote X9 (0.61 [95% CI = 0.54-0.69]) and Zotero (0.62 [95% CI = 0.54-0.69]) and poor for Rayyan (0.41 [95% CI = 0.36-0.47]).

CONCLUSION:

Choosing the most suitable tool depends on its interface's characteristics, the algorithm to identify and exclude duplicates, and the transparency of the process. Therefore, Rayyan, Mendeley, and SRA proved to be accurate enough for the systematic reviews' deduplication step.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Algoritmos Tipo de estudio: Prognostic_studies / Systematic_reviews Límite: Humans Idioma: En Revista: J Clin Epidemiol Asunto de la revista: EPIDEMIOLOGIA Año: 2022 Tipo del documento: Article

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Algoritmos Tipo de estudio: Prognostic_studies / Systematic_reviews Límite: Humans Idioma: En Revista: J Clin Epidemiol Asunto de la revista: EPIDEMIOLOGIA Año: 2022 Tipo del documento: Article