Marketing analysis of medicines based on the active substance St. John's wort (Hypericum perforatum L.).
Bibliographic record
Abstract
Annotation. Medicinal plant St. John's wort (Hypericum perforatum L) is widely used in medicine in many countries of the world as a natural antidepressant, and due to its antitumor and cytotoxic effects. Research is being actively conducted on the use of St. John's wort in other areas of medicine. The purpose of the study is a marketing analysis of medicinal products that are registered in Ukraine with the active ingredient St. John's wort (Hypericum perforatum L.). The research material was data from the State Register of Medicinal Products of Ukraine in 2022, declared wholesale and retail prices in Ukraine as of October 2022. The research methods were frequency and content analysis. According to the ATS classification of medicinal products with the active ingredient St. John's wort on the pharmaceutical market of Ukraine, the presence of 4 groups has been established: A - agents affecting the digestive system and metabolism, N - agents affecting the nervous system, L - antineoplastic and immunomodulating agents, G-agents affecting the genitourinary system and sex hormones. According to the declared wholesale and retail prices as of October 2022, various forms of release of medicines were established, namely tablets, capsules, collections, solutions, herbs, drops, tinctures, syrups and balms. The cost of various dosage forms with the active ingredient St. John's wort was determined. It was established that the cost of 1 package ranges from UAH 10.57 to UAH 474.21. As a result of the content analysis, an assortment of medicinal forms registered on the territory of Ukraine and consisting of 27 trade names was established. The medical fees are presented by 5 TN and occupy 18.52% of the pharmaceutical market of Ukraine. According to the ATS classification, the largest group of drugs with the active ingredient St. John's wort is group A – drugs that affect the digestive system and metabolism. During the analysis of manufacturers of modern dosage forms it was established that the ratio of domestic and foreign production is 3:1, where domestic dosage forms make up 74%. In the future, it is planned to carry out a more detailed content analysis of drugs, taking into account accessibility for the consumer/patient and the development of new complex drugs that based on St. John's wort.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".