Responding to the Pandemic of Falsified Medicines
Bibliographic record
Abstract
Over the past decade, the number of countries reporting falsified (fake, spurious/falsely labeled/counterfeit) medicines and the types and quantities of fraudulent drugs being distributed have increased greatly. The obstacles in combatting falsified pharmaceuticals include 1) lack of consensus on definitions, 2) paucity of reliable and scalable technology to detect fakes before they reach patients, 3) poor global and national leadership and accountability systems for combating this scourge, and 4) deficient manufacturing and regulatory challenges, especially in China and India where fake products often originate. The major needs to improve the quality of the world's medicines fall into three main areas: 1) research to develop and compare accurate and affordable tools to identify high-quality drugs at all levels of distribution; 2) an international convention and national legislation to facilitate production and utilization of high-quality drugs and protect all countries from the criminal and the negligent who make, distribute, and sell life-threatening products; and 3) a highly qualified, well-supported international science and public health organization that will establish standards, drug-quality surveillance, and training programs like the U.S. Food and Drug Administration. Such leadership would give authoritative guidance for countries in cooperation with national medical regulatory agencies, pharmaceutical companies, and international agencies, all of which have an urgent interest and investment in ensuring that patients throughout the world have access to good quality medicines. The organization would also advocate strongly for including targets for achieving good quality medicines in the United Nations Millennium Development Goals and Sustainable Development Goals.
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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 | 0.030 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.011 | 0.018 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.021 | 0.019 |
| Insufficient payload (model declined to judge) | 0.022 | 0.007 |
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".