Маркетинговий аналіз фармацевтичного ринку антидіабетичних лікарських засобів в Україні
Bibliographic record
Abstract
Diabetes mellitus is an important public health issue. Statistics show a rapid growth in the number of patients each year, which increases the financial burden on the state’s economy. Therefore, systematic marketing monitoring of the pharmaceutical market of drugs for diabetes is appropriate and permanently relevant. The aim of the research was to study the pharmaceutical market of antidiabetic drugs in Ukraine as of the first quarter of 2022. Materials and methods. The nomenclature range of drugs was analyzed in accordance with the State Register and the Anatomical-Therapeutic-Chemical Classification. The objects of research were information on synthetic hypoglycemic drugs registered in Ukraine, as well as herbal medicines belonging to the category A10 “Antidiabetic drugs”. Analytical and comparative methods, as well as marketing analysis and generalization of information were applied. Results. The structure of the pharmaceutical market of medicines from category A10 “Antidiabetic drugs” is formed by mono-drugs, their share is 84.2 %, while combined drugs make 12.9 %, and only 2.9 % belongs to medicinal plant raw materials and their mixtures. Among mono-drugs, the most numerous by the number of trade names is the group of sulfonylureas and biguanides, the percentage of which is 33.9 %. The pharmaceutical market of antidiabetic drugs in Ukraine is import-dependent, as the share of foreign medicines from 24 importing countries is 59.3 %. Domestic manufacturers of drugs for type 2 diabetes are 10 pharmaceutical companies, among which production output is dominated by JSC “Farmak” – 31.9 %. Preparations based on medicinal plant raw materials are manufactured by PJSC “Liktravy” and PJSC Pharmaceutical Factory “Viola” in the form of mixtures and medicinal raw materials. Conclusions. The nomenclature ranges of drugs, which according to the ATC classification belong to the category A10 “Antidiabetic drugs”, are analyzed and the structure of the segment is described. The range of herbal antidiabetic drugs has been studied and their share in the structure of the pharmaceutical market of Ukraine has been determined. The prevalence of foreign-made drugs over domestic ones has been established.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.008 |
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".