Ethics framework for treatment use of investigational drugs
Bibliographic record
Abstract
BACKGROUND: Expanded access is the use of investigational drugs (IDs) outside of clinical trials. Generally it is performed in patients with serious and life-threatening diseases who cannot be treated satisfactorily with authorized drugs. Legal regulations of expanded access to IDs have been introduced among others in the USA, the European Union (EU), Canada and Australia. In addition, in the USA an alternative to expanded access is treatment under the Right-to-Try law. However, the treatment use of IDs is inherently associated with a number of ethically relevant problems. MAIN TEXT: The objective of this article is to present a coherent framework made up of eight requirements which have to be met for any treatment use of an ID to be ethical. These include a justified need for the use of an ID, no threat to clinical development of the ID, adequate scientific evidence to support the treatment, patient's benefit as the primary goal of the use of an ID, informed decision of a patient, fair access of patients to IDs, independent review, as well as the dissemination of treatment results. CONCLUSIONS: While this framework is essentially consistent with the legal regulations of expanded access of the USA, the EU, Canada and Australia, it is substantially wider in scope because it addresses some important issues that are not covered by the regulations. Overall, the framework that we developed minimizes the risks and threats, and maximizes potential benefits to each of the four key stakeholders involved in the treatment use of IDs including patients, doctors, drug manufacturers, and society at large.
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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 | 0.120 | 0.079 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.007 | 0.027 |
| Scholarly communication | 0.013 | 0.007 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.017 | 0.013 |
| Insufficient payload (model declined to judge) | 0.003 | 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".