Objective: The objective of the study was to test the overall effectiveness of a simplified search strategy (SSS) for updating systematic reviews.
Study Design And Methods: We identified nine systematic reviews undertaken by our research group for which both comprehensive and SSS updates were performed. Three relevant performance measures were estimated, that is, sensitivity, precision, and number needed to read (NNR).
Results: The update reference searches for all nine included systematic reviews identified a total of 55,099 citations that were screened resulting in final inclusion of 163 randomized controlled trials. As compared with reference search, the SSS resulted in 8,239 hits and had a median sensitivity of 83.3%, while precision and NNR were 4.5 times better. During analysis, we found that the SSS performed better for clinically focused topics, with a median sensitivity of 100% and precision and NNR 6 times better than for the reference searches. For broader topics, the sensitivity of the SSS was 80% while precision and NNR were 5.4 times better compared with reference search.
Conclusion: SSS performed well for clinically focused topics and, with a median sensitivity of 100%, could be a viable alternative to a conventional comprehensive search strategy for updating this type of systematic reviews particularly considering the budget constraints and the volume of new literature being published. For broader topics, 80% sensitivity is likely to be considered too low for a systematic review update in most cases, although it might be acceptable if updating a scoping or rapid review.
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http://dx.doi.org/10.1016/j.jclinepi.2017.06.005 | DOI Listing |
Acta Neuropsychiatr
January 2025
IRCCS Ospedale Policlinico San Martino, Genoa, Italy.
Objective: Time distortions characterise severe mental disorders, exhibiting different clinical and neurobiological manifestations. This systematic review aims to explore the existing literature encompassing experimental studies on time perception in patients with bipolar disorder (BD), considering psychopathological and cognitive correlates.
Methods: Studies using an experimental paradigm to objectively measure the capacity to judge time have been searched for.
Afr J Prim Health Care Fam Med
December 2024
Department of Internal Medicine, Prince Mshiyeni Memorial Hospital, Durban.
Background: Tuberculosis (TB) remains a leading cause of mortality in low-resource settings and poses a diagnostic challenge in human immunodeficiency virus (HIV)-negative populations because of limitations in traditional diagnostic methods such as sputum smear microscopy (SSM) and sputum Xpert Ultra. There is a lack of effective, non-invasive diagnostic options for TB diagnosis in HIV-negative populations. This scoping review explores the potential of urinary lipoarabinomannan (ULAM) as a point-of-care diagnostic tool for Mycobacterium tuberculosis (MTB) in HIV-negative individuals.
View Article and Find Full Text PDFNurs Open
January 2025
Department of Midwifery, Faculty of Nursing and Midwifery, Tabriz University of Medical Sciences, Tabriz, Iran.
Aim: The present study was conducted to determine the effect of non-pharmacological interventions before cataract surgery on preoperative anxiety.
Design: Systematic review and meta-analysis.
Methods: Five databases were systematically searched until 9 June, 2024.
Eur Heart J Digit Health
January 2025
Massachusetts General Hospital, 55 Fruit St, Boston, MA 02114, USA.
Aims: Accurate prediction of clinical outcomes following percutaneous coronary intervention (PCI) is essential for mitigating risk and peri-procedural planning. Traditional risk models have demonstrated a modest predictive value. Machine learning (ML) models offer an alternative risk stratification that may provide improved predictive accuracy.
View Article and Find Full Text PDFEur Heart J Digit Health
January 2025
School of Life Course & Population Sciences, King's College London, SE1 1UL London, UK.
Cardiovascular disease (CVD) remains a major cause of mortality in the UK, prompting the need for improved risk predictive models for primary prevention. Machine learning (ML) models utilizing electronic health records (EHRs) offer potential enhancements over traditional risk scores like QRISK3 and ASCVD. To systematically evaluate and compare the efficacy of ML models against conventional CVD risk prediction algorithms using EHR data for medium to long-term (5-10 years) CVD risk prediction.
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