Test of a Homeopathic Algorithm for COVID-19: the Importance of a Broad Perspective.
Journal
Homeopathy : the journal of the Faculty of Homeopathy
ISSN: 1476-4245
Titre abrégé: Homeopathy
Pays: Germany
ID NLM: 101140517
Informations de publication
Date de publication:
Feb 2023
Feb 2023
Historique:
pubmed:
22
8
2022
medline:
25
1
2023
entrez:
21
8
2022
Statut:
ppublish
Résumé
Most of the symptoms of coronavirus disease 2019 (COVID-19) are covered by large repertory rubrics and hence many remedies have been proposed as "genus epidemicus". The aim of this study was to combine the information from various data collections to prepare a COVID-19 Bayesian mini-repertory/an algorithm-based application (app) and test it. In July 2021, 1,161 COVID-19 cases from 100 practitioners globally were combined. These data were used to calculate "condition-confined" likelihood ratios (LRs) for 59 symptoms of COVID-19. Out of these, 35 symptoms of the 11 medicines that had at least 20 cases each were considered. The information was entered in a spreadsheet (algorithm) to calculate combined LRs of specific combinations of symptoms. The algorithm contained the medicines The algorithm was re-tested on 358 cases, and concordance was seen in 288 cases. On analysis of the data, bias was noticed in the The Bayesian mini-repertory and app is based on qualitative clinical experiences of various doctors in COVID-19 and gives indications for specific medicines for common COVID-19 symptoms. It is freely available [English: https://hpra.co.uk/; Spanish: https://hpra.co.uk/es ] for further testing and utilization by the profession.
Sections du résumé
BACKGROUND
BACKGROUND
Most of the symptoms of coronavirus disease 2019 (COVID-19) are covered by large repertory rubrics and hence many remedies have been proposed as "genus epidemicus". The aim of this study was to combine the information from various data collections to prepare a COVID-19 Bayesian mini-repertory/an algorithm-based application (app) and test it.
METHODS
METHODS
In July 2021, 1,161 COVID-19 cases from 100 practitioners globally were combined. These data were used to calculate "condition-confined" likelihood ratios (LRs) for 59 symptoms of COVID-19. Out of these, 35 symptoms of the 11 medicines that had at least 20 cases each were considered. The information was entered in a spreadsheet (algorithm) to calculate combined LRs of specific combinations of symptoms. The algorithm contained the medicines
RESULTS
RESULTS
The algorithm was re-tested on 358 cases, and concordance was seen in 288 cases. On analysis of the data, bias was noticed in the
CONCLUSION
CONCLUSIONS
The Bayesian mini-repertory and app is based on qualitative clinical experiences of various doctors in COVID-19 and gives indications for specific medicines for common COVID-19 symptoms. It is freely available [English: https://hpra.co.uk/; Spanish: https://hpra.co.uk/es ] for further testing and utilization by the profession.
Identifiants
pubmed: 35988581
doi: 10.1055/s-0042-1746196
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
22-29Informations de copyright
Faculty of Homeopathy. This article is published by Thieme.
Déclaration de conflit d'intérêts
None declared.