Evidence-based data for endometriosis management are limited. Experiments are excluded without adequate animal models. Data are limited to symptomatic women and occasional observations. Hormonal medical therapy cannot be blinded if recognised by the patient. Randomised controlled trials are not realistic for surgery, since endometriosis is a variable disease with low numbers. Each diagnosis and treatment is an experiment with an outcome, and experience is the means by which Bayesian updating, according to the past, takes place. If the experiences of many are similar, this holds more value than an opinion. The combined experience of a group of endometriosis surgeons was used to discuss problems in managing endometriosis. Considering endometriosis as several genetically/epigenetically different diseases is important for medical therapy. Imaging cannot exclude endometriosis, and diagnostic accuracy is limited for superficial lesions, deep lesions, and cystic corpora lutea. Surgery should not be avoided for emotional reasons. Shifting infertility treatment to IVF without considering fertility surgery is questionable. The concept of complete excision should be reconsidered. Surgeons should introduce quality control, and teaching should move to explain why this occurs. The perception of information has a personal bias. These are the major problems involved in managing endometriosis, as identified by the combined experience of the authors, who are endometriosis surgeons.
Keywords: Bayesian statistics; endometriosis diagnosis; endometriosis surgery; endometriosis therapy; evidence-based medicine.