The diagnosis of endometriosis is a probability based on age, history, symptoms, clinical examination, ultrasound and MRI imaging, laboratory results such as CA-125 or other markers, and surgical observations, until confirmed by pathology. The diagnosis of endometriosis and management decisions are based on probabilities with multiple variables.  Similarly, all decisions during therapy are based on multivariable probabilities 1.

The randomised clinical trial is poorly suited for rare events and cannot handle a multivariate input because of randomisation issues 2.  In the pyramid of evidence, experience is considered a personal opinion with little value. As a consequence, guidelines for the management of endometriosis, a disease without an appropriate animal model  and thus without experimentation, are limited and variable.

 Clinical experience is a Bayesian process of updating management by past experiences. The collective experience is the cumulative experience of many clinicians. Since all decisions are binary, e.g. to do surgery or not,  collective experience translates into a sigmoidal relationship between the input, eg severity of pain, and the decision, eg to do a laparoscopy. Other aspects of collective experience are heuristics and surgical skills. Therefore, guidelines based on trial evidence should be updated for multivariability of decisions, collective Experience, heuristics, and Bayesian thinking 3

Reference List

1.           Koninckx PR, Ussia A, Miller C, et al. How to bridge the gap between evidence-based and clinical management of endometriosis. Journal of Endometriosis and Uterine Disorders 2025:100123.

2.           Koninckx PR, Koushik A, Tulandi T. Truth, Soft Lies, Statistics, and Experience in Managing Endometriosis. J Obstet Gynaecol Can 2024;46:102421.

3.           Koninckx PR, Ussia A, Stepanian A, et al. The Evidence-Based Medicine Management of Endometriosis Should Be Updated for the Limitations of Trial Evidence, the Multivariability of Decisions, Collective Experience, Heuristics, and Bayesian Thinking. Journal of Clinical Medicine 2025;14:248.