The intersection of prescription drugs and medical devices: the evaluation and funding challenges of two categories of emerging health technologies
Bibliographic record
Abstract
Health technology assessments (HTAs) are policy analysis frameworks contributing to the approval, reimbursement, and rollout of biotechnology and pharmaceuticals. New innovations in health technologies expose gaps in reimbursement and implementation guidelines. We defined two types of emerging health technologies: (1) therapeutic innovations, such as drug-device combination products or nondrug alternatives to prescription drugs and (2) disruptive health innovations such as novel surgeries and gene replacement therapies. We aimed to determine delineated definitions for these categories through a comprehensive review of HTA guidelines across 20 nations. Utilizing databases such as International Network of Agencies for HTA, International Society for Pharmacoeconomics and Outcomes Research, and European Medical Agency, we identified products falling within these categories. Real-world case studies highlighted the inadequacies stemming from the absence of clear definitions and proposed solutions to enhance current HTA guidelines. These shortcomings apply at the state or provincial level in addition to national jurisdictions as existing funding structures and silos fail to accommodate the unique attributes of these technologies.
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 | 0.105 | 0.217 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.019 | 0.021 |
| Science and technology studies | 0.004 | 0.024 |
| Scholarly communication | 0.030 | 0.035 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.010 | 0.011 |
| Insufficient payload (model declined to judge) | 0.004 | 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".