A Review On Regulatory Requirements To Prevent Counterfeit Drugs In India
Bibliographic record
Abstract
Counterfeit drugs are a serious issue nowadays, which has been burdening for developing countries and has a significant impact on the health of people. The Indian drug regulatory agency has published the details of the counterfeited products found in recent years, the data indicates an alarming situation in the country to achieve a well-becoming level of attention between policymakers, researchers and the pharmaceutical industry. The present paper shows a thorough review of relevant and incidental literature from different agencies to reach the counterfeiting of drugs, its type, statistics, incidents and deficiency in Indian regulation. Different country regulations like U.S, Europe and Canada have also discussed in comparison with Indian regulation which shows that how India should adopt the technologies present in other countries to combat counterfeit medicine. Different technologies exist for prevention for counterfeitings of drugs like overt, covert and trace and track technologies; serialization and blockchain. In this article, the main focus is given on serialization and blockchain which will help from entering the counterfeit drugs into the supply chain because of unique code put on each box of drugs to trace them.
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 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".