Rapid reviews methods series: Guidance on literature search

Klerings, Irma; Robalino, Shannon; Liquitay, Camila Micaela Escobar; Sommer, Isolde; Gartlehner, Gerald; Devane, Declan; Waffenschmidt, Siw

doi:10.1136/bmjebm-2022-112079

Cited by 36 publications

(29 citation statements)

References 37 publications

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“…It may be most appropriate in the context of rapid reviews, where there is a need to complete a review in a short timeframe with limited resources to screen studies. In such reviews, search strategies that maximise precision over sensitivity may be selected 23,23 . However, using such a strategy risks missing a large percentage of eligible ITS studies, which should be reported as a limitation of the review.…”

Section: Discussionmentioning

confidence: 99%

Development of a search filter to retrieve reports of interrupted time series studies from MEDLINE and PubMed

Nguyen,

McKenzie,

Turner

et al. 2023

Preprint

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Background: Interrupted time series (ITS) studies contribute importantly to systematic reviews of population-level interventions. However, there is no search filter designed to identify only ITS studies from bibliographic databases. We aimed to develop and validate search filters to retrieve ITS studies in MEDLINE and PubMed. Methods: A set of 1,017 known ITS studies (published 2013-2017) were analysed using text mining to generate candidate terms. A population set of 1,398 time-series studies were used to select discriminating terms. Various combinations of candidate terms were iteratively tested to generate three search filters. An independent set of 700 ITS studies were used to validate the filters' sensitivities. The filters were test-run in Ovid MEDLINE; their retrieved records were randomly screened for ITS studies to determine their precision. Finally, all three MEDLINE filters were translated to PubMed format and their sensitivities in PubMed were estimated. Results: Three search filters were created: a precision-maximising filter with high precision (78%; 95% CI 74%-82%) but moderate sensitivity (63%; 59%-66%), useful for rapid reviews; a sensitivity-and-precision-maximising filter with high sensitivity (81%; 77%-83%) but lower precision (32%; 28%-36%), useful when a balance of expediency and comprehensiveness is required; and a sensitivity-maximising filter with high sensitivity (88%; 85%-90%) but likely very low precision, useful for search strategies accompanying specific content terms. Conclusion: Our filters strike different balances between comprehensiveness and screening workload and suit different research needs. To improve retrieval of ITS studies, authors need to identify ITS designs in titles more frequently.

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Section: Discussionmentioning

confidence: 99%

Development of a search filter to retrieve reports of interrupted time series studies from MEDLINE and PubMed

Nguyen,

McKenzie,

Turner

et al. 2023

Preprint

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“…We conducted a swift study 19 to identify all the documented cases of fungal infections resulting from CARD9 deficiency. The case reports were selected using PubMed, Embase, Scopus and Google Scholar database between October 2009 and November 2022.…”

Section: Methodsmentioning

confidence: 99%

CARD9 mutations in patients with fungal infections: An update from the last 5 years

Dantas,

Cintra,

Lucini

et al. 2024

Mycoses

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BackgroundAutosomal recessive deficiency in the caspase recruitment domain‐containing protein 9 (CARD9) is a congenital immunological condition that leads to susceptibility to mucocutaneous and invasive fungal infections. There is growing incidence of fungal infections in patients with CARD9 deficiency, a phenomenon that is increasingly recognised.ObjectivesThis study aimed to assess the frequency, geographic distribution and nature of mutations in patients with CARD9 deficiency, based on published papers in the literature until March 2023.MethodsWe swiftly conducted a study to pinpoint every documented instance of fungal infections arising from CARD9 deficiency. We selected case reports from the databases of PubMed, Embase, Scopus and Google Scholar spanning the period from October 2009 to March 2023.ResultsWe analysed 90 cases of fungal infections and identified 32 mutations in the CARD9 gene. Notably, the homozygous (HMZ) p.Q295X (c.883C > T) mutation was associated with an increased risk of candidiasis. In contrast, the HMZ p.Q289X (c.865C > T) mutation is linked to a higher risk of dermatophytosis. We observed differences in the geographical distribution of these mutations. The primary mutations found in African patients differ from those in Asian patients. Specifically, Asian patients exhibit a broader spectrum of CARD9 mutations than African patients.ConclusionsThe diversity of mutations observed in the 90 cases revealed 32 distinct variations, emphasising the unique genetic alterations in the CARD9 gene associated with specific geographical areas and the corresponding prevalence of fungal infections.

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“…The development of the literature search and screening strategy ( Klerings et al, 2023 ), along with data extraction, was initially designed by the first two authors. A review meeting involving all four authors was conducted to assess the research design strategy.…”

Section: Methodsmentioning

confidence: 99%

An adaptive data-driven architecture for mental health care applications

Sundaram,

Subramaniam,

Ab Hamid

et al. 2024

PeerJ

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Background In the current era of rapid technological innovation, our lives are becoming more closely intertwined with digital systems. Consequently, every human action generates a valuable repository of digital data. In this context, data-driven architectures are pivotal for organizing, manipulating, and presenting data to facilitate positive computing through ensemble machine learning models. Moreover, the COVID-19 pandemic underscored a substantial need for a flexible mental health care architecture. This architecture, inclusive of machine learning predictive models, has the potential to benefit a larger population by identifying individuals at a heightened risk of developing various mental disorders. Objective Therefore, this research aims to create a flexible mental health care architecture that leverages data-driven methodologies and ensemble machine learning models. The objective is to proficiently structure, process, and present data for positive computing. The adaptive data-driven architecture facilitates customized interventions for diverse mental disorders, fostering positive computing. Consequently, improved mental health care outcomes and enhanced accessibility for individuals with varied mental health conditions are anticipated. Method Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, the researchers conducted a systematic literature review in databases indexed in Web of Science to identify the existing strengths and limitations of software architecture relevant to our adaptive design. The systematic review was registered in PROSPERO (CRD42023444661). Additionally, a mapping process was employed to derive essential paradigms serving as the foundation for the research architectural design. To validate the architecture based on its features, professional experts utilized a Likert scale. Results Through the review, the authors identified six fundamental paradigms crucial for designing architecture. Leveraging these paradigms, the authors crafted an adaptive data-driven architecture, subsequently validated by professional experts. The validation resulted in a mean score exceeding four for each evaluated feature, confirming the architecture’s effectiveness. To further assess the architecture’s practical application, a prototype architecture for predicting pandemic anxiety was developed.

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Rapid reviews methods series: Guidance on literature search

Cited by 36 publications

References 37 publications

Development of a search filter to retrieve reports of interrupted time series studies from MEDLINE and PubMed

Development of a search filter to retrieve reports of interrupted time series studies from MEDLINE and PubMed

CARD9 mutations in patients with fungal infections: An update from the last 5 years

An adaptive data-driven architecture for mental health care applications

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