Substandard and Falsified Medical Products: Time to Arrest Their Growth
Bibliographic record
Abstract
Dear Sir Substandard and falsified medical products refer to those medications or products that are developed with the intention to duplicate or substitute certified products.[ 1 ] [ 2 ] Based on the incidents reported in the last quarter of the year 2022 and in January 2023, it was found that in excess of 300 people lost their lives across seven nations owing to the consumption of cough syrups, which had diethylene glycol and ethylene glycol in significantly higher concentrations.[ 3 ] This calls for the need to take stringent and urgent measures to prevent, identify, and urgently respond to the incidents of such events in the future.[ 2 ] [ 3 ] As it is a problem that involves multiple stakeholders, it is quite essential that we adopt a strategy that targets the involvement of all the concerned stakeholders.[ 1 ] [ 4 ] This has to begin with the involvement of policymakers, which must take necessary measures to identify and remove all substandard products, ensure circulation of only approved medical products, carry out periodic inspections of medicine production sites, expand the surveillance network in pharmaceutical markets, and ensure strict implementation of existing legal provisions, failing which offenders should be strictly punished.[ 4 ] [ 5 ] The next important stakeholder is the manufacturing industry, which should purchase required chemicals from licensed suppliers, ensure holistic testing of the received chemicals, and maintain quality assurance standards, and all the records making the entire process accountable.[ 2 ] [ 3 ] [ 4 ] Finally, the distributors should check for signs of any contaminated product, engage in the distribution and sale of products approved by registered agencies, maintain records, and essentially involve trained personnel to monitor the entire process.[ 1 ] [ 2 ] [ 3 ] The role of each of these stakeholders is important and indispensable.[ 3 ] [ 4 ] In conclusion, the circulation of substandard and falsified medical products in the commercial market is a major global public health hazard. This calls for the need to adopt a multipronged approach targeting different stakeholders to safeguard human lives. Publication History Article published online: 05 September 2023 © 2023. The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution License, permitting unrestricted use, distribution, and reproduction so long as the original work is properly cited. (https://creativecommons.org/licenses/by/4.0/) Thieme Medical and Scientific Publishers Pvt. Ltd. A-12, 2nd Floor, Sector 2, Noida-201301 UP, India
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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 | 0.010 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.023 | 0.009 |
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".