Managing Risk in Developing Transplant Immunosuppressive Agents: The New Regulatory Environment
Bibliographic record
Abstract
Recent adverse experience with a number of medications after their approval, including rofecoxib, erythropoietin and rosiglitazone, has led to an increased focus on safety in drug development in the postmarketing setting. The result was implementation of new measures to address perceived deficits in the system for drug approval and postmarketing safety. The resulting legislation introduced risk evaluation and mitigation strategies (REMS) and postmarketing requirements (PMRs). Although these initiatives have the potential to improve patient outcomes, many healthcare practitioners are not yet familiar with REMS or PMRs or may have misconceptions regarding their goals and limitations. REMS is a program to manage known or potential serious risks associated with pharmaceutical products and is designed to ensure that the benefits of using a particular product outweigh the risks. Although the concepts underlying REMS and PMRs are not novel, the FDA now has legal authority to enforce such measures as part of the drug approval process. This article outlines the objectives and limitations of REMS and PMRs, with a focus on how these regulatory measures may impact the clinical specialty of transplantation. The article also briefly describes efforts to address aspects of drug safety less amenable to management through REMS and PMRs.
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 | 0.077 | 0.098 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.017 |
| Scholarly communication | 0.015 | 0.014 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.010 | 0.017 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".