On the potential value of eHTA: a commentary on “Defining Early Health Technology Assessment: Building Consensus Using Delphi Technique”
Bibliographic record
Abstract
The HTAi Health Technology Assessment (eHTA) Working Group's (WG) development of a consensus definition of early eHTA, as reported in Grutters et al. (1), represents a major step towards the establishment of eHTA as a distinct subdiscipline of HTA. In a global landscape in which growth in pharmaceutical spending is driven by the increasing number of high-cost specialty drugs (2-6), and where the cost of new entrants is not systematically associated with their clinical benefit (7;8), broader uptake of eHTA by pharmaceutical innovators offers a route to improving the value delivered by our collective investments in drug research and development (R&D). As we argue in this commentary, the WG's report provides a coherent framework within which to further define appropriate eHTA methods for specific use cases as well as eHTA's relationship to other decision-making tools currently used by health technology innovators and funders.
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 | 0.211 | 0.465 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.015 | 0.041 |
| Scholarly communication | 0.013 | 0.026 |
| Open science | 0.016 | 0.014 |
| Research integrity | 0.084 | 0.107 |
| 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".