scholarly journals Interventions Encouraging the Use of Systematic Reviews in Clinical Decision-Making: A Systematic Review

2010 ◽  
Vol 26 (4) ◽  
pp. 419-426 ◽  
Author(s):  
Laure Perrier ◽  
Kelly Mrklas ◽  
Sasha Shepperd ◽  
Maureen Dobbins ◽  
K. Ann McKibbon ◽  
...  
Keyword(s):  
Systematic Review ◽  
Decision Making ◽  
Systematic Reviews ◽  
Clinical Decision Making ◽  
Clinical Decision

scholarly journals Focused echocardiography: a systematic review of diagnostic and clinical decision-making in anaesthesia and critical care

Anaesthesia ◽  
2016 ◽  
Vol 71 (9) ◽  
pp. 1091-1100 ◽  
Author(s):  
J. Heiberg ◽  
D. El-Ansary ◽  
D. J. Canty ◽  
A. G. Royse ◽  
C. F. Royse
Keyword(s):  
Systematic Review ◽  
Decision Making ◽  
Critical Care ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Focused Echocardiography

In Conversation… Prof. Sam Cortese on ADHD, Coh-fit study

2020 ◽  
Keyword(s):  
Decision Making ◽  
Systematic Reviews ◽  
Adolescent Psychiatry ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Child And Adolescent ◽  
Child And Adolescent Psychiatry

Professor Sam Cortese discusses ADHD, research in relation to clinical decision-making in child and adolescent psychiatry, the importance of systematic reviews, and his work on the European ADHD Guidelines Group and its work on ADHD management during the covid-19 pandemic. Includes transcription, and links.

Extracts from The Cochrane Library: Nasal saline irrigations for the symptoms of chronic rhinosinusitis

2007 ◽  
Vol 137 (4) ◽  
pp. 532-534 ◽  
Author(s):  
Martin J. Burton ◽  
Lee D. Eisenberg ◽  
Richard M. Rosenfeld
Keyword(s):  
Decision Making ◽  
Head And Neck ◽  
Chronic Rhinosinusitis ◽  
Systematic Reviews ◽  
Clinical Decision Making ◽  
Cochrane Review ◽  
Clinical Decision ◽  
Neck Surgery ◽  
Cochrane Library ◽  
The Cochrane Library

The “Cochrane Corner” is a quarterly section in the journal that highlights systematic reviews relevant to otolaryngology–head and neck surgery, with invited commentary to highlight implications for clinical decision-making. This installment features a Cochrane Review entitled “Nasal saline irrigations for the symptoms of chronic rhinosinusitis,” which shows that saline irrigations are well-tolerated and could be included as a treatment adjunct for the symptoms of chronic rhinosinusitis.

scholarly journals Predicting infection and sepsis; what predictors have been used to train machine learning algorithms? A systematic review

2021 ◽  
Vol 29 (Supplement_1) ◽  
pp. i18-i18
Author(s):  
N Hassan ◽  
R Slight ◽  
D Weiand ◽  
A Vellinga ◽  
G Morgan ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Systematic Review ◽  
Decision Making ◽  
Quality Assessment ◽  
Clinical Decision Making ◽  
Data Extraction ◽  
Learning Algorithms ◽  
Clinical Decision ◽  
Machine Learning Algorithms

Abstract Introduction Sepsis is a life-threatening condition that is associated with increased mortality. Artificial intelligence tools can inform clinical decision making by flagging patients who may be at risk of developing infection and subsequent sepsis and assist clinicians with their care management. Aim To identify the optimal set of predictors used to train machine learning algorithms to predict the likelihood of an infection and subsequent sepsis and inform clinical decision making. Methods This systematic review was registered in PROSPERO database (CRD42020158685). We searched 3 large databases: Medline, Cumulative Index of Nursing and Allied Health Literature, and Embase, using appropriate search terms. We included quantitative primary research studies that focused on sepsis prediction associated with bacterial infection in adult population (>18 years) in all care settings, which included data on predictors to develop machine learning algorithms. The timeframe of the search was 1st January 2000 till the 25th November 2019. Data extraction was performed using a data extraction sheet, and a narrative synthesis of eligible studies was undertaken. Narrative analysis was used to arrange the data into key areas, and compare and contrast between the content of included studies. Quality assessment was performed using Newcastle-Ottawa Quality Assessment scale, which was used to evaluate the quality of non-randomized studies. Bias was not assessed due to the non-randomised nature of the included studies. Results Fifteen articles met our inclusion criteria (Figure 1). We identified 194 predictors that were used to train machine learning algorithms to predict infection and subsequent sepsis, with 13 predictors used on average across all included studies. The most significant predictors included age, gender, smoking, alcohol intake, heart rate, blood pressure, lactate level, cardiovascular disease, endocrine disease, cancer, chronic kidney disease (eGFR<60ml/min), white blood cell count, liver dysfunction, surgical approach (open or minimally invasive), and pre-operative haematocrit < 30%. These predictors were used for the development of all the algorithms in the fifteen articles. All included studies used artificial intelligence techniques to predict the likelihood of sepsis, with average sensitivity 77.5±19.27, and average specificity 69.45±21.25. Conclusion The type of predictors used were found to influence the predictive power and predictive timeframe of the developed machine learning algorithm. Two strengths of our review were that we included studies published since the first definition of sepsis was published in 2001, and identified factors that can improve the predictive ability of algorithms. However, we note that the included studies had some limitations, with three studies not validating the models that they developed, and many tools limited by either their reduced specificity or sensitivity or both. This work has important implications for practice, as predicting the likelihood of sepsis can help inform the management of patients and concentrate finite resources to those patients who are most at risk. Producing a set of predictors can also guide future studies in developing more sensitive and specific algorithms with increased predictive time window to allow for preventive clinical measures.

Sign in / Sign up

Export Citation Format

Share Document