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Record W3023796674 · doi:10.1111/jphs.12357

Some lessons from the COVID‐19 pandemic

2020· editorial· en· W3023796674 on OpenAlexaboutno aff
Albert I. Wertheimer

Bibliographic record

VenueJournal of Pharmaceutical Health Services Research · 2020
Typeeditorial
Languageen
FieldPsychology
TopicCOVID-19 and Mental Health
Canadian institutionsnot available
Fundersnot available
KeywordsMedicinePandemicCoronavirus disease 2019 (COVID-19)2019-20 coronavirus outbreakSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)VirologyOutbreakInternal medicineInfectious disease (medical specialty)

Abstract

fetched live from OpenAlex

Now and then, over the past dozen years or so, one might hear a random comment about how precarious it is to be nearly completely dependent on the drug supply from distant countries. Yet, nothing was done because it appeared to nearly all that the global trade system was functioning properly and Americans as well as citizens in the European Union seem to have an adequate supply of the medications they needed at quite attractive prices. Now in 2020, this concern has moved from a theoretical question of concern to an actual problem that might have consequences for millions worldwide. The virus first was observed in China where it became so prevalent that many factories and distribution facilities were paralysed due to employee illness and absence causing closures for anywhere from a few weeks to several months. Now, we can look back with perfect 20 : 20 hindsight and realize that putting all of our (proverbial) eggs in one basket was not such a good idea. In the USA and in the EU, over 80% of the prescribed medications are from generic sources. The sellers of these generic drugs purchase the active ingredients (API) from firms primarily in China and India, and formulate the final dosage form locally, dependent upon foreign, imported ingredients. Now, we are coming to the conclusion that it makes sense to perhaps pay a little more to reactivate a domestic pharmaceutical synthesis and generic manufacturing capability in the USA and in the EU. Western governments can give tax and other financial incentives for firms building domestic capacity. For example, during World War II in the 1940s, Norway had virtually no domestic pharmaceutical production capacity and was forced to endure shortages of numerous critical medications. Their lesson was to see that such shortages would never occur again by the creation of a government-owned system of warehouses that would hold approximately a three-month supply given the usual levels of consumption. More recently, the US Food and Drug Administration (FDA) and the European Medicines Agency (EMA) have agreed to a mutual recognition scheme where products not registered in one area can be imported from the other area, since it assumes that the drug marketing approval process is of approximately equal rigour on both sides of the Atlantic Ocean. But, if every country relies on API from the same blocked source in China, that is of no help. Recently, the Indian government has suggested banning the export of hydroxychloroquine which may provide some benefit to COVID-19 patients. Understandably, they desire to have an adequate supply for their own population. Given the unpredictability of crises requiring foreign-sourced drugs, it makes perfect sense to foster a domestic capacity. What country would be satisfied having to purchase all of its military equipment and weapons from other countries that might not always align with US or EU national policies? So, in summary, it might be that no one was harmed by our foreign drug dependence this time, but kicking the can down the street for some future administration to deal with in the future is not acceptable. We must demand the creation of local pharmaceutical (API) productive capacity in the very near future, and it does not have to entirely exist within one single country. For example, Canada, the USA and Mexico could share such a plan, as could several countries in Europe with long-standing friendships.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.022
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: none
Teacher disagreement score0.020
Threshold uncertainty score0.067

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.022
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0040.008
Scholarly communication0.0080.017
Open science0.0020.004
Research integrity0.0130.023
Insufficient payload (model declined to judge)0.0200.006

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.

Opus teacher head0.439
GPT teacher head0.659
Teacher spread0.220 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEditorial

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".

Quick stats

Citations0
Published2020
Admission routes1
Has abstractyes

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