Clinical Diagnosis In Ayurveda Pdf Download __LINK__
Annabel Chatfield
We are in such a situation today that we need to use both ICD and ACD when reporting clinical outcomes in Ayurveda. Without modern scientific terminology of diseases, it may become difficult to not only communicate effectively, but also to negotiate with regulatory authorities, insurance, and mainstream science. On the other hand, if we ignore the Ayurvedic classification and nomenclature of diseases, we are at the risk of losing the unique individualized multimodal approach of Ayurveda to maintain health and treat diseases.
Ayurveda and modern medicine are derived from different epistemological and ontological premises. Therefore, the approach to diagnosis of diseases as well as nomenclature differs. It is quite impossible to make one to one correlations or pick up equivalent terms. For example, anemia in modern medicine is not exactly Pāṇḍu, which in a broader sense includes many other clinical conditions. Similarly, Prameha is not exactly diabetes mellitus. On the other hand, there are many diseases that can be correlated in a fairly straightforward manner. Hemorrhoids and fistula in ano are examples, which correlate well with the conditions known as Arśas and Bhagandara, respectively, in Ayurveda.
We can find that diseases can be classified once again into three categories from this viewpoint. Broadly, there are the diseases that have been listed in the texts (ukta gada) and the ones that have not been listed (anukta gada). Some of the listed diseases can be identified exactly as described in the texts. We may call these diseases as yathokta vyadhi. There are diseases that have some semblance to textual descriptions but are not exactly matching with the clinical presentation. These diseases may be called as ayathokta vyadhi.
The need for Ayurvedic nomenclature and classification of diseases is obviously to preserve the individualized approach to treatment. Without an Ayurvedic diagnosis, it is difficult for an Ayurvedic physician to visualize a complete Ayurvedic treatment. When the disease is diagnosed in terms of modern medicine, Ayurvedic physicians tend to apply the principles of modern medical treatment when choosing Ayurvedic therapies. For example, if a condition is diagnosed as anemia instead of Pāṇḍu, the physician tends to choose medications like Mandūra Vataka or Lohasava to target the iron deficiency, whereas the majority of the formulations mentioned in Pāṇḍu do not work on this principle.
On the other hand, modern diagnosis and treatment outcome measures may be helpful in objectively assessing response to Ayurvedic treatment, and especially to engage in dialog with the scientific community. However, in clinical studies, this approach may create difficulties for the treating Ayurvedic physician because he/she cannot depend on the modern diagnosis to derive the individualized multimodal treatments, which will vary from patient to patient even if the modern medical diagnosis is the same for all patients and is comparable at the baseline. Ayurvedic treatments are based on a reflexive, recursive, and iterative algorithm. Yet another pitfall is that reliance on modern outcome measures may result in neglecting the outcome measures that would help in understanding overall effect of patient-centered care.
While ICD nomenclature cannot be ignored by Ayurvedic physicians, one cannot escape from the fact that Ayurvedic physicians need to develop an internally consistent system of disease nomenclature and classification, which will enable physicians to arrive at Ayurvedic diagnosis of a disease and thereby a whole medical system strategy for treatment. It is also desirable that studies are done on consistency of diagnosis amongst physicians in the Ayurvedic community to identify consensus and differences on diagnosis and treatment.
Results: 170 responses were received from 252 eligible clients: an overall response rate of 67%. Test-retest data for 57 respondents gave a reliability coefficient of 0.83 [0.69-0.91]. The outcomes of consultation were independent of the mode of interaction (in-person vs. telehealth) and whether pulse diagnosis information was available. 85% were at least Satisfied with their overall consultation experience. The mean difference in health from before consultation to the survey was 1.2 [1.0-1.4] on a 7-point scale. 61% reported Much to Exceptional benefit. On average, 63% rated themselves as at least Somewhat Better in terms of physical well-being, emotional well-being, sleep, digestion, bowel function, fitness and energy. These findings were buttressed by clinically significant changes on 4 clinical assessment questionnaires among 60 clients. Engaging in follow-up and adopting recommendations were associated with better outcomes.
The case presented is to exemplify the use of Ayurveda diagnostic techniques in treating diseases. Among the diagnostic tools, Upashaya-Anupashaya (understanding of aggravating and relieving factors) is useful during unclear pathology and symptomatology. Upashaya is the betterment, and Anupashaya is the aggravation of disease by medication, diet, and lifestyle. The case presented here is of a 60-year-old male with sudden weakness of the left upper and lower extremities, with no other symptoms and significant previous medical history. The provisional diagnosis was Pakshavadha (hemiplegia) and treated through Brimhana (nourishing therapy), which led to the aggravation of the disease condition. Anupashaya by Brimhana treatment confirmed the diagnosis to KaphavritaVata (occlusion of Vata by Kaphadosha) and was treated with Rukshana (dryness inducing therapies), Pachana (enhancing digestion) with significant relief in symptoms within four days, with no recurrence. The significance of Upashaya-Anupashaya in the confirmation of disease and treatment is highlighted in this case.
It's important to discuss any Ayurvedic treatments that you use with your doctor. Women who are pregnant or nursing, or people who are thinking of using Ayurvedic therapy to treat a child, should consult their healthcare provider. It is important to make sure that any diagnosis of a disease or condition has been made by a healthcare provider who has substantial conventional medical training and experience with managing that disease or condition. While Ayurveda can have positive effects when used as a complementary therapy in combination with standard, conventional medical care, it should not replace standard, conventional medical care, especially when treating serious conditions.
In the clinical settings, interrater reliability is the degree to which two or more raters agree on a diagnosis of the same subject under identical assessment conditions. Reliability studies are necessary because they provide information about the quality of measurements and also play an important role in the process of developing effective diagnostic procedures [2].
Thus, pulse, tongue, and prakriti assessment are integral parts of an Ayurvedic diagnosis. To incorporate Ayurvedic diagnostic criteria into a clinical study to improve the confidence in the clinical findings, it is, however, necessary to confirm the validity and reliability of Ayurvedic diagnostic criteria [14, 15].
The prakriti has specific physical, physiological, and psychological characteristics based on dosha attributes. Detailed information is available in [9, 10, 17]. In this study, doctors assessed these characteristics by inspection, interrogation, and palpation to determine the prakriti for the subject (Table 1). After the clinical examination, doctors wrote their final prakriti assessment on the assessment form.
Fifteen registered doctors, who have been practicing in Sri Sri College of Ayurvedic Science & Research Hospital, conducted the study. Ten were M.D. (Ayurveda) holders, two had M.S. in Ayurveda, and three had a B.A.M.S. (Bachelor of Ayurveda, Medicine and Surgery) in Ayurveda and had completed a pulse diagnosis course (Figure 1).
For each data set and each pair of doctors, we tested the null hypothesis of random rating, where the probability that the doctor assigns a particular diagnosis to a subject does not depend on the subject. A minimal requirement for agreement between doctors is that each of them performs significantly better than a random rating. Therefore, if the data do not show strong evidence against , this suggests a poor level of reliability. The value can be viewed as an alternative to the Landis-Koch scale for interpreting the kappa statistics, where large values correspond to low reliability. The value for each pairwise kappa, that is, the probability of getting at least as favorable a weighted kappa as the observed, assuming , was computed by calculating the empirical distribution of the pairwise kappa under random permutation of subject for each doctor (Figure 4). Specifically, we used the estimate , where is the number of pairwise kappas computed under permutation, that is, larger or equal to the observed, and is the number of permutations. The number of permutations used was 50,000. A Bonferroni correction was used to account for multiple hypothesis testing.
The distribution of all pairwise kappas for pulse, tongue, and prakriti assessment is seen in Figure 5. Figure 5(d) shows a Venn diagram of the significant values in each dataset. No pairwise kappa was significant in more than one dataset. There is no common significant value for any diagnosis. For example, the pair of doctors who did better for prakriti assessment (12 significant values) did not show the same result for tongue or pulse examination.
To see whether pairs of doctors with a high degree of reliability (i.e., a high pairwise kappa) in one dataset also concur in another dataset, scatter plots of the pairwise kappa values between different diagnoses were made and shown in Figure 7. More formally, a test for the null hypothesis of zero correlation was carried out. No statistically significant correlation was observed. That means that the hypothesis that stated the correlation is zero cannot be rejected. Hence, there is no evidence that a pair of doctors who agreed on one type of diagnosis also agreed on the other types of diagnoses or vice versa.
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