A Study on Pharmaceutical Drug Recall
Bibliographic record
Abstract
The present study describes the pharmaceutical drug recall in different five countries to evaluate the drug recall that occurred in the last three successive years. The different countries have different regulations for drug recall. Drug product recall is an action taken to withdraw or remove a batch or an entire production run of drug product from distribution or use to return them to manufacturer.it is usually done due to deficiency in quality, safety and efficacy. In the USA, guidelines for drugs product recall are described under 21 CFR Parts 7, 107 and 1270. In Australia, guidelines for drugs product recall are described under section 65F of trade practices act 1974. In Canada, it includes under section 25 of Natural Health Products Regulations (NHPR). In India it includes under para 27 and 28 of schedule M. In South Africa SAHPRA (South African Health Products Regulatory Authority) guidelines are responsible for regulations of drug product recall. Majority of drug recalls occur in the United states due to various reasons. In 2020-2022 total 257 drugs were recalled in Last three years. In Canada and Australia 220 and 25 drugs are recalled respectively. India and South Africa have recalled 2 and 21 drugs respectively. By the observation we can conclude that India and South Africa have a smaller number of recalls. In the USFDA number of drug recalls are decreasing due to following up the laws and regulatory.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 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".