The plague of unexpected drug recalls and the pandemic of falsified medications in cardiovascular medicine as a threat to patient safety and global public health: A brief review
Bibliographic record
Abstract
Valsartan, losartan, and irbesartan, are widely used in the treatment strategies of cardiovascular medicine diseases, including hypertension and heart failure. Recently, many formulations for the aforementioned diseases contained active pharmaceutical ingredients and had been abruptly recalled from the market due to safety concerns mainly associated with unwanted impurities - nitrosamines, which are highly carcinogenic substances accidentally produced during manufacturing. Along with cardiovascular medications, formulations containing ranitidine were also recalled from the market. This poses a particular threat to public health due to the non-prescription status of these drugs. Regulatory authorities, including the Food and Drug Administration and European Medicines Agency among others, have taken action to minimize patient risk and improve the manufacturing quality as well as re-checking current guidelines and recommendations. While these steps are necessary to avoid further recalls, authorities should remember the growing concerns of patients regarding the safety and efficacy of pharmacotherapy. Apart from the genuine manufacturing mistakes mentioned above, falsified and counterfeit medications should be at the heart of global attention. The lack of a well-accepted definition of falsified/counterfeit medications has impeded political and scientific efforts to mitigate risk of this phenomenon. Falsified Medicines Directive should be considered the most pivotal legislation recently enacted to harmonize international cooperation. In summary, one should remember that only international and direct collaboration between patients, stakeholders, and authorities be considered a remedy for a pandemic of falsified medicines and plague of unexpected recalls due to safety concerns.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".