Is it Ethical to Prescribe Generic Immunosuppressive Drugs to Renal Transplant Patients?
Bibliographic record
Abstract
PURPOSE OF THE REVIEW: This review was conducted to determine the ethical acceptability of prescribing generic immunosuppressive drugs to renal transplant patients. SOURCES OF INFORMATION: The literature search was conducted using Pubmed and Google Scholar. FINDINGS: The use of generic immunosuppressive drugs (ISDs) in transplantation is a controversial topic. There is a consensus among transplant societies that clinical data is lacking and that caution should be exercised. The reluctance to use generic ISDs in organ transplantation is partly related to the fact that most are "critical dose drugs", and that either low dosing or overdosing could have serious adverse consequences for both patients and society (i.e., the loss of scarce organs). In this paper, we examine the various ethical issues involved such as distributive justice, physician duties, risks versus benefits, conflict of interest, informed consent, and logistical and economic issues. LIMITATIONS: Our analysis was limited by the paucity of clinical data on generic ISDs and the absence of health economics studies to quantify the benefits of prescribing generic ISDs. IMPLICATIONS: Our study led us to conclude that it would be ethical to prescribe generic ISDs provided certain conditions were met. These include regulatory safeguards to minimize the risks of substitution; education of patients; and further clinical and health economics studies to better inform clinicians, patients and society of the risks and costs related to drug substitution.
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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 | 0.034 | 0.156 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".