Analysis of disinfectant use by medical organizations of the Republic of Tatarstan in the context of changes in the epidemiological situation 2019–2022
Bibliographic record
Abstract
BACKGROUND: Disinfection is an important aspect of ensuring epidemiological safety in medical organizations, particularly during the COVID-19 pandemic. AIM: To analyze the disinfectant use in state medical organizations of the Republic of Tatarstan from 2019 to 2022, considering changes in the epidemiological situation related to COVID-19, and provide recommendations based on the analysis. MATERIALS AND METHODS: Electronic auction data from zakupki.gov.ru were used to analyze disinfectant purchases by state medical organizations in the Republic of Tatarstan from 2019 to 2022. Data analysis involved the application of descriptive statistical methods. RESULTS: A total of 118 electronic auctions were analyzed. A relationship between the nature of the epidemiological situation and the selection of disinfectants for purchase based on their chemical composition and intended use was identified. Before the pandemic, medical organizations preferred quaternary ammonium compound disinfectants and antibacterial soaps for hand hygiene. During the COVID-19 pandemic, their use of chlorine-based disinfectants, hydrogen peroxide, and alcohol-based hand sanitizers containing 60–69% ethanol increased. As the epidemiological situation stabilized, a discernible shift in preferences toward compound disinfectants was noted. Antiseptics with an alcohol content 70% are increasingly sought after. CONCLUSIONS: Medical organizations in the Republic of Tatarstan promptly respond to changes in the epidemiological situation by implementing necessary modifications to antiepidemic measures. When developing procurement strategies, not only the requirements of sanitary legislation but also the latest scientific findings in the field of disinfectology must be considered.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".