Humanitarian Use Device and Humanitarian Device Exemption regulatory programs: pros and cons
Bibliographic record
Abstract
The US FDA established the Humanitarian Use Device (HUD) and Humanitarian Device Exemption (HDE) program to encourage medical device firms to address rare diseases. Despite being in existence for over a decade, there has only been one peer-reviewed publication examining this field. The objective of this report is to investigate how the HUD/HDE program differs from the standard regulatory system, discuss its potential advantages and disadvantages, and to speculate which humanitarian devices will be brought to market within the next 5 years. A total of 40 semistructured interviews with stakeholders, representing approximately half (n = 20, 49%) of the firms that have successfully obtained HDE-approved products, were performed in order to acquire the primary data for this paper. There appear to be short-term gains and long-term drains associated with launching humanitarian devices to market. This report aims to provide sponsors with information that may allow them to make better decisions during their product development of humanitarian devices and may, hopefully, also play a role in encouraging other sponsors to take the necessary steps forward in helping to find treatments for patients with rare diseases.
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 | 0.056 | 0.073 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.006 | 0.003 |
| 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".