Critical Appraisal of Medical Literature

Marchevsky, David

doi:10.1007/978-1-4615-4205-6

Cited by 4 publications

(1 citation statement)

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“…Most basic EBM monographs explain how to do this; others are entirely devoted to critical appraisal (Elwood 1988(Elwood , 1998Dixon et al . 1997;Crombie 1998;Marchevsky 2000;Ajetunmobi 2002;U.K. Courses and Information on Critical Appraisal 2005).…”

Section: Critical Appraisal Of Evidence Itself Vs Appraisal Of Its Umentioning

confidence: 99%

The hard art of soft science: Evidence‐Based Medicine, Reasoned Medicine or both?

Jenicek

2006

Evaluation Clinical Practice

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In the past 14 years, Evidence-Based Medicine (EBM) has enjoyed unprecedented developments and gained widespread acceptance among health professionals. However, should we be content with producing, critically appraising and using the best evidence available for our understanding of health problems and decision making about them? Are our convictions about EBM's relevance, our conviction and intellectual satisfaction with its mastery and adoption enough? Should we continue pushing forward along this promising path, or should we further diversify the content and scope of EBM? Is EBM the only way to view medicine in the near future? This paper presents some options to choose from in terms of direction and content as well as questions to answer given the current EBM crossroads. More intensive and extensive EBM combined with 'other features'-based medicines may be the preferred strategy to follow in the future to determine the development, use and evaluation of EBM. Argument-based medicine or Reasoned Medicine is one of the options that can be integrated into the mainstream of medical reasoning and decision making.

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Section: Critical Appraisal Of Evidence Itself Vs Appraisal Of Its Umentioning

confidence: 99%

The hard art of soft science: Evidence‐Based Medicine, Reasoned Medicine or both?

Jenicek

2006

Evaluation Clinical Practice

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Potential types of bias when estimating causal effects in environmental research and how to interpret them

Konno,

Gibbons,

Lewis

et al. 2024

Environ Evid

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To inform environmental policy and practice, researchers estimate effects of interventions/exposures by conducting primary research (e.g., impact evaluations) or secondary research (e.g., evidence reviews). If these estimates are derived from poorly conducted/reported research, then they could misinform policy and practice by providing biased estimates. Many types of bias have been described, especially in health and medical sciences. We aimed to map all types of bias from the literature that are relevant to estimating causal effects in the environmental sector. All the types of bias were initially identified by using the Catalogue of Bias (catalogofbias.org) and reviewing key publications (n = 11) that previously collated and described biases. We identified 121 (out of 206) types of bias that were relevant to estimating causal effects in the environmental sector. We provide a general interpretation of every relevant type of bias covered by seven risk-of-bias domains for primary research: risk of confounding biases; risk of post-intervention/exposure selection biases; risk of misclassified/mismeasured comparison biases; risk of performance biases; risk of detection biases; risk of outcome reporting biases; risk of outcome assessment biases, and four domains for secondary research: risk of searching biases; risk of screening biases; risk of study appraisal and data coding/extraction biases; risk of data synthesis biases. Our collation should help scientists and decision makers in the environmental sector be better aware of the nature of bias in estimation of causal effects. Future research is needed to formalise the definitions of the collated types of bias such as through decomposition using mathematical formulae.

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Real-Time Inferential Analytics Based on Online Databases of Trends: A Breakthrough within the Discipline of Digital Epidemiology in Dentistry and Dental Anatomy

Al-Imam¹,

U²,

Al-Hadithi³

et al. 2018

Preprint

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BACKGROUND Epidemiological sciences have been evolving at an exponential rate paralleled only by the comparable growth within the discipline of data science. Digital epidemiological studies are playing a vital role in medical science analytics for the past few decades. To date, there are no published attempts at deploying the use of real-time analytics in connection with the disciplines of Dentistry or Medicine. AIMS AND OBJECTIVES We deployed a real-time statistical analysis in connection with topics in Dental Anatomy and Dental Pathology represented by the maxillary sinus, posterior maxillary teeth, related oral pathology. The purpose is to infer the digital epidemiology based on a continuous stream of raw data retrieved from Google Trends database. MATERIALS AND METHODS Statistical analysis was carried out via Microsoft Excel 2016 and SPSS version 24. Google Trends database was used to retrieve data for digital epidemiology. Real-time analytics and the statistical inference were based on encoding a programming script using Python high-level programming language. A systematic review of the literature was carried out via PubMed-NCBI, the Cochrane Library, and Elsevier databases. RESULTS The comprehensive review of databases of the literature, based on specific keywords search, yielded 491813 published studies. These were distributed as 488884 (PubMed-NCBI), 1611 (the Cochrane Library), and 1318 (Elsevier). However, there was no single study attempting real-time analytics. Nevertheless, we succeeded in achieving an automated real-time stream of data accompanied by a statistical inference based on data extrapolated from Google Trends. CONCLUSION Real-time analytics are of considerable impact when implemented in biological and life sciences as they will tremendously reduce the required resources for research. Predictive analytics, based on artificial neural networks and machine learning algorithms, can be the next step to be deployed in continuation of the real-time systems to prognosticate changes in the temporal trends and the digital epidemiology of phenomena of interest.

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Critical Appraisal of Medical Literature

Cited by 4 publications

References 0 publications

The hard art of soft science: Evidence‐Based Medicine, Reasoned Medicine or both?

The hard art of soft science: Evidence‐Based Medicine, Reasoned Medicine or both?

Potential types of bias when estimating causal effects in environmental research and how to interpret them

Real-Time Inferential Analytics Based on Online Databases of Trends: A Breakthrough within the Discipline of Digital Epidemiology in Dentistry and Dental Anatomy

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