One of the best ways to grade recommendations in the medical field is by using a two-level system: strong and weak recommendations. 

Please find below the simplified approach for the purpose of this Consensus Conference:

  • Strong Recommendation: This is used when the benefits of an intervention clearly outweigh the potential harms, or vice versa. A strong recommendation indicates that most patients should receive the recommended intervention, or it should be avoided in most cases.    
  • Weak Recommendation: Also known as "Conditional", this is used when the balance between benefits and potential harms is less certain, or when patient values and preferences may vary. A weak recommendation indicates that different options may be reasonable, and the decision should be based on individual patient circumstances, values, and preferences.    

This simplified approach is less time consuming and allows for clear communication and easy understanding of recommendations.    

Each recommendations, whether strong or weak, is accompanied by the quality of evidence based on factors such as study design (e.g., RCT vs. retrospective series), risk of bias, consistency, directness, and precision. The quality of evidence can be graded as very low, low, moderate or high. It is not uncommon to report a strong recommendation associated with low quality of evidence (e.g. smoking cessation prior to transplantation). 

The purpose of the GRADE approach is to provide a systematic and transparent process for evaluating evidence and making healthcare recommendations, helping to ensure that healthcare decisions are based on the best available evidence and are informed by various contextual factors.  

The modified GRADE approach as reported by Murad et al. suits best for the purpose of this Consensus Conference as systematic reviews are very unlikely to provide information derived from meta-analyses and hence the recommendations will be based on qualitative rather than quantitative analysis of the literature search findings. 


The MIOT.CC Team