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Record W7057960742

Marketing research of the market of drugs for the treatment of hepatobiliary system diseases in Ukraine during the covid-19 pandemic

2021· article· en· W7057960742 on OpenAlexaboutno aff

Bibliographic record

VenueThe Scientific Issues of Ternopil Volodymyr Hnatiuk National Pedagogical University Series pedagogy · 2021
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMagnetic confinement fusion research
Canadian institutionsnot available
Fundersnot available
KeywordsPandemicCompendiumUkrainianMarket researchPharmacovigilanceProtocol (science)Disease
DOInot available

Abstract

fetched live from OpenAlex

Introduction. Since the beginning of the COVID-19 pandemic, doctors around the world, including Ukraine, have seen an increase in liver disease, which, incidentally, leads to complications of coronavirus infection. This can be both acute liver damage by the virus itself and hepatotoxic effects of medicines used in COVID-19. The treatment of liver and biliary tract diseases remains an urgent task for Ukraine, given the epidemic situation that exists today in the country. The purpose of our research was to study the range of drugs for the treatment of liver and biliary tract diseases registered in the pharmaceutical market of Ukraine since the beginning of the COVID-19 pandemic. Materials and methods. The data of information resources, such as the State Register of Drugs, Compendium of Drugs, etc., which have been generalized by means of marketing methods, structural, statistical, and graphic analyzes, are used in the work. Results and discussion. A special place in the treatment of hepatobiliary pathology is occupied by medicines that belong to the group of hepatoprotectors. There are several classifications of hepatoprotectors. For example, SV Okovytyi proposes to divide hepatoprotectors by origin. According to the classification proposed by ON Minushkin, LV Maslovskyi, AA Bukshuk hepatoprotectors are divided by mechanism of action. Thus hepatoprotectors can be of plant or animal origin, medicines that contain amino acids and essential phospholipids, medicines of synthetic origin. We have analyzed the Ukrainian market of hepatoprotective medicines. The analysis was performed in group A05 "Bile and liver therapy" (according to the ATC classification). Data are for September 2021. Both imported and domestic drugs are represented in the Ukrainian market. A total of 73 trade names of hepatoprotectors have been registered so far. The first place in the number of names of medicines for the treatment of hepatobiliary system diseases, which are on the market of Ukraine, is occupied by Germany, the second - India, and the third - Czech Republic and Canada. In general, hepatoprotective medicines from 16 countries are present on the Ukrainian market. The first place in prevalence is occupied by medicines of plant origin. Second place take ursodeoxycholic acid preparations, and in third place are preparations of essential phospholipids. Solid dosage forms for oral administration (tablets, capsules, granules) occupy more than half of the market of hepatoprotective medicinal products in Ukraine. Most hepatoprotective drugs are over-the-counter. Conclusions. Given the state of treatment of hepatobiliary system diseases, which has not lost its relevance during the COVID-19 pandemic, the situation in the pharmaceutical market in terms of range of hepatoprotectors registered in Ukraine has been analyzed. It is demonstrated that hepatoprotective drugs are widely represented in the pharmaceutical market of Ukraine. The market is saturated with original and generic drugs, medicines of both imported and domestic production are presented; both prescription and over-the-counter drugs in various dosage forms are available. Keywords: hepatobiliary system, liver disease, hepatoprotectors, market analysis DOI: 10.5281/zenodo.5761206

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.448
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.000

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.

Opus teacher head0.105
GPT teacher head0.386
Teacher spread0.281 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2021
Admission routes1
Has abstractyes

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