Bibliographic record
Abstract
www.thelancet.com Vol 386 October 3, 2015 1327 John Clark is a busy man. The former deputy assistant secretary of Homeland Security (US customs and immigration) now heads up Pfi zer’s Global Security team—a team that tracks down people who manufacture and sell counterfeit drugs. And he has a question for doctors: do you know where your patients get their drugs from? The offi cial term for these types of medicines is spurious/falsely-labelled/ falsifi ed/counterfeit (SFFC) drugs. It’s not a title that rolls off the tongue, but it’s one that doctors in developing countries are more than aware of with estimates of the prevalence of counterfeit drugs in some parts of Africa and Asia reaching as high as 70%. In high-income countries it might not be at the forefront of every practitioner’s mind, but the rise of online pharmacies in Europe and the USA could change that. WHO estimates that 50% of the drugs for sale on the internet are fake and even though the online dispensaries might look legitimate, a survey of 10 000 of them done by America’s National Association of Boards of Pharmacy (NABP) found that 9938 did not comply with NABP patient safety and pharmacy practice standards or US state and federal laws. Most said they were based in Canada but were really a front for illegal off shore operations. Clark told The Lancet that he has seen estimates of the markets’ worth ranging from US$70 billion to $200 billion, but the truth of the matter, is that “no one has their hands around the exact size of the global problem”. And no one knows exactly how many people buy prescription drugs online but some estimates predict it is up to 5 million in America and around 2 million in the UK. A 2009 survey of general practitioners in the UK found 25% had treated a patient who had experienced an adverse eff ect from a drug bought online.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.167 | 0.048 |
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".