Bibliographic record
Abstract
BACKGROUND: Compassionate use is the use of unapproved drugs outside of clinical trials. So far, compassionate use regulations have been introduced in the US, Canada, many European countries, Australia and Brazil, and treatment on a compassionate use basis may be performed in Japan and China. However, there are important differences between relevant regulations in individual countries, particularly that approval by a research ethics committee (institutional review board) is a requirement for compassionate use in some countries (e.g. the US, Spain, and Italy), but not in others (e.g. Canada, the UK, France, and Germany). DISCUSSION: The main objective of this article is to present aspects of compassionate use that are important for the discussion of the role of research ethics committees in the review of compassionate use. These aspects include the nature of compassionate use, potential risks to patients associated with the use of drugs with unproven safety and efficacy, informed consent, physicians' qualifications, and patient selection criteria. Our analysis indicates that the arguments for mandatory review substantially outweigh the arguments to the contrary. CONCLUSIONS: Approval by a research ethics committee should be obligatory for compassionate use. The principal argument against mandatory ethical review of compassionate use is that it is primarily a kind of treatment rather than biomedical research. Nonetheless, compassionate use is different from standard clinical care and should be subject to review by research ethics committees. First, in practice, compassionate use often involves significant research aspects. Second, it is based on unapproved drugs with unproven safety and efficacy. Obtaining informed consent from patients seeking access to unapproved drugs on a compassionate use basis may also be difficult. Other important problems include the qualifications of the physician who is to perform treatment, and patient selection criteria.
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 | 0.404 | 0.589 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.006 | 0.025 |
| Scholarly communication | 0.012 | 0.014 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.016 | 0.016 |
| Insufficient payload (model declined to judge) | 0.006 | 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".