APPROACHES TO EXPIRY DATES DETERMINATION OF EXTEMPORALNON-STERILE MEDICINES
Bibliographic record
Abstract
The article is devoted to the study of approaches to establishing the expiry dates of non-sterile medicines manufactured at pharmacies. It has been determined that in some countries a separate concept of “beyond-use-date” (English) is used to indicate the expiry date of extemporal medicines. This concept implies the date (or time and date) after which a prepared sterile or non-sterile medicinal preparation cannot be used, stored or transported. In the guidelines and regulations of some foreign countries (Australia, the USA, Canada) the maximum expiry dates of the dosage forms prepared at the pharmacy are presented. However, these expiry dates do not take into account chemical stability of medicines. They mainly characterize possibility of microorganisms multiplication and occurrence of hydrolysis. Pharmaceutical employees of the countries abovementioned determine the expiry date for each extemporal drug and indicate it on the label. To do this, they take into account additional factors that can effect stability of a medicine during storage and lead to physical and chemical changes. At the same time, specialists can refer to published information, independently conduct research or give determination of the medicine expiry date to outsourcing.
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".