Bibliographic record
Abstract
The paper presents the assortment trend and marketing analysis of veterinary medicinal products for the prevention and treatment of helminthic infestations in horses registered in the Russian Federation. Anthelmintics are antiparasitic drugs for the fight against helminths. The analysis of these drugs was carried out on the example of the veterinary pharmacies "Horsevet" and "Vetlek". According to the data obtained, it was established that the Russian market has a significant range of medicinal anthelmintic drugs (41 names) used for the prevention and treatment of helminth infections in horses. The domestic manufacturer occupies a leading position among the presented range of products – 34.1±1.87 %. On the market of veterinary drugs, there are imported anthelmintic drugs represented by such manufacturing countries as Belarus, Argentina, Brazil, Holland, Spain, Canada, etc. In the study of drugs by the number of pharmacologically active substances, it was found that monocomponent drugs account for 63.4±0.38 %, and multicomponent drugs – 36.6±0.85 %. The most common are soft dosage forms – 63.4±3.78 % (26 trade names in the form of flavored pastes and gels), which makes it easier to give the drug. Anthelmintic drugs in most cases are potent drugs, so most of them have a number of contraindications for use (85.4±4.95 %). 14.6±0.95 % of drugs have no contraindications. The use of all registered anthelmintic drugs for horses at the recommended doses does not cause changes in vital functions in the animal body.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".