Counting Polar Symptoms: How to Represent Results? - Rutten, Lex; Eizayaga, José; Kaur, Harleen; Lamba, Chetna Deep; Sachdeva, Jyoti; Khurana, Anil; Oberai, Praveen; Manchanda, Rajkumar; Raizada, Sonia; Chaudhary, Sujata; Shinde, Vaishali; Ramanan, Vinitha Edavattath; Kundu, Chittaranjan; Pal, Pratha Pratim; Kiranmayee, Rompicherla GR; Sahoo, Amulya Ratan; Rajeshekhar, Bodankar; Shil, Ratan Chandra; Mahajan, Nidhi (Homeopathy, 2020)
Background Polar symptoms (PS)—symptoms with opposite values—are frequently used in homeopathy, but have many misleading entries in the repertory. This is caused by using absolute occurrence of symptoms, causing the same medicine to appear in both (opposite) symptom rubrics, and by lack of comparison with other medicines. Some PS, like ‘aversion/desire for sweets’ have a frequency distribution that is not evenly distributed around the neutral value: a desire for sweets is much more common than aversion. A desire for sweets is an indication for a specific medicine only if this desire occurs more frequently in this specific medicine population than in the remainder of the population. We need to find the best way to represent this difference.Methods A multi-centre, explorative, prospective, observational study was conducted by nine centres of the Central Council for Research in Homoeopathy. Two-hundred and sixteen patients were enrolled with chronic cough lasting more than 8 weeks, and received usual homeopathic care. During intake, 30 general PS, 27 polar cough symptoms and 3 non-polar cough symptoms were checked. Different ways of representing results were explored, including two quantities borrowed from mechanics: Centre of Mass (CoM) and Leverage.Results At the fourth follow-up, three medicines with more than 10 cases with good results were identified: 20 Phosphorus, 19 Pulsatilla and 13 Sulphur. The mean value of the frequency distribution of some symptoms in the whole sample was considerably different from the neutral value. Comparing a medicine population with the remainder of the respective population can give results that differ from polarity analysis. For some symptoms, the ‘distance’ (Leverage) between the CoMs of the medicine population and the remainder of the population was clearer than the likelihood ratio (LR).Conclusion If the LR value is not clear about the prognostic value in PS, notions from mechanics such as CoM and Leverage can clarify how to interpret a polar symptom.
Keywords: polar symptoms; prognostic factor; Bayes' theorem; homeopathy; likelihood ratio; centre of mass; leverage