Book Review: Pharmaceutical Risk Management: Practical Applications
Bibliographic record
Abstract
Given several high-profile recalls in recent years of pharmaceuticals by the US Food and Drug Administration (FDA), the subject of pharmaceutical risk management has become increasingly important.Pharmaceutical risk management refers to manufacturers creating special tools and programs to ensure the safe use of certain high risk products.The FDA's Amendments Act (FDAAA) of 2007 prompted the Food and Drug Law Institute (FDLI) to publish Pharmaceutical Risk Management: Practical Applications (2008), a follow up to their 2003 publication, A Framework for Pharmaceutical Risk Management.The 2008 edition is a multi-author work written by experienced risk managers who have organized risk management programs as consultants or industry executives; some are alumni of the FDA.It is important to note that this is not a second edition of the 2003 book, but rather an extension of the research and methods presented in the original, with an emphasis on practical applications of risk management principles.The purpose of this publication is to educate pharmaceutical companies, consultants, and other drug industry stakeholders on the new rules for Risk Evaluation and Mitigation strategies (REMS) during preand post-marketing drug development.
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 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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.039 | 0.028 |
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".