Fair, just and compassionate: A pilot for making allocation decisions for patients requesting experimental drugs outside of clinical trials
Bibliographic record
Abstract
Patients have received experimental pharmaceuticals outside of clinical trials for decades. There are no industry-wide best practices, and many companies that have granted compassionate use, or 'preapproval', access to their investigational products have done so without fanfare and without divulging the process or grounds on which decisions were made. The number of compassionate use requests has increased over time. Driving the demand are new treatments for serious unmet medical needs; patient advocacy groups pressing for access to emerging treatments; internet platforms enabling broad awareness of compelling cases or novel drugs and a lack of trust among some that the pharmaceutical industry and/or the FDA have patients' best interests in mind. High-profile cases in the media have highlighted the gap between patient expectations for compassionate use and company utilisation of fair processes to adjudicate requests. With many pharmaceutical manufacturers, patient groups, healthcare providers and policy analysts unhappy with the inequities of the status quo, fairer and more ethical management of compassionate use requests was needed. This paper reports on a novel collaboration between a pharmaceutical company and an academic medical ethics department that led to the formation of the Compassionate Use Advisory Committee (CompAC). Comprising medical experts, bioethicists and patient representatives, CompAC established an ethical framework for the allocation of a scarce investigational oncology agent to single patients requesting non-trial access. This is the first account of how the committee was formed and how it built an ethical framework and put it into practice.
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 | 0.236 | 0.352 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.015 | 0.019 |
| Scholarly communication | 0.016 | 0.019 |
| Open science | 0.004 | 0.020 |
| Research integrity | 0.010 | 0.017 |
| Insufficient payload (model declined to judge) | 0.025 | 0.005 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".