The politics of the EU pharmaceutical industry
Bibliographic record
Abstract
"This thoughtful and comprehensive book represents the best work I have seen on the current situation concerning medication policies in the EU. It is not just that this is a very up-to-date compendium of facts and data across a wide variety of domains that impact on pharmaceutical regulation. The book is also strong on analysis of those facts as well."Jerry Avorn, Harvard Medical School. "This book offers a comprehensive examination of approaches to manage pharmaceutical expenditures in Europe. It is a must-read for those who seek to understand and navigate the changing regulatory environment for medicines in the European Union."Bernie O'Brien, McMaster University, Canada. The rising cost of pharmaceutical expenditures in many European countries is of concern to governments required to make effective use of health care budgets. Taking a broad perspective that encompasses institutional, political and supranational aspects of pharmaceutical regulation, this book examines approaches used to manage pharmaceutical expenditure across Europe and what impact these strategies have had on efficiency, quality, equity and cost of pharmaceutical care.Regulating Pharmaceuticals in Europe is an important book for students of health policy, regulation and management, and for health managers and policy makers.The editors:Elias Mossialos is Brian Abel-Smith Professor of Health Policy at the London School of Economics and Political Science and a Research Director of the European Observatory on Health Systems and Policies.Monique Mrazek is a Health Economist (Europe and Central Asia region) for the World Bank and formerly a Research Officer in Health Economics for the European Observatory on Health Systems and Policies.Tom Walley is Professor of Clinical Pharmacology at the University of Liverpool and Director of the UK National Health Technology Assessment Programme.Contributors: Julia Abelson, Christa Altenstetter, Vittorio Bertele’, Christine Bond, Marcel L. Bouvy, Colin Bradley, Steve Chapman, Anna Dixon, Michael Drummond, Pierre Durieux, Edzard Ernst, Armin Fidler, Eric Fortess, Richard Frank, Silvio Garattini, Leigh Hancher, Ebba Holme Hansen, Steve Hudson, Kees de Jonchere, Panos Kanavos, Sjoerd Kooiker, Jean-Marc Leder, Graham Lewis, Donald W. Light, Alistair McGuire, Elias Mossialos, Monique Mrazek, Maria Pia Orru', Govin Permanand, Guenka Petrova, Munir Pirmohamed, Dennis Ross-Degnan, Frans Rutten, Steven Soummerai, David Taylor, Sarah Thomson, Tom Walley.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.015 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".