Defining early health technology assessment: building consensus using Delphi technique
Bibliographic record
Abstract
Although early health technology assessment (HTA) is increasingly being used to guide and inform decisions on product development, a consensus definition is currently lacking. A working group under the HTA International Society was established to develop a consensus-based definition of early HTA. The working group developed a definition using an iterative process that comprised five stages of work and included a two-round Delphi survey with 133 respondents in the first and 99 respondents in the second round of the survey, with various backgrounds and levels of expertise. Following this process, the working group reached the first consensus-based definition of early HTA, which is an HTA conducted to inform decisions about subsequent development, research, and/or investment by explicitly evaluating the potential value of a conceptual or actual health technology. In total, 86 (87 percent) of the 99 panelists who participated in the second round of the Delphi survey either strongly agreed or agreed with this definition. This consensus definition represents an important milestone in early HTA. It will enhance the uniformity of terminology, increasing the visibility of research and policy in this field. We also hope that it will act as a catalyst sparkling further research and developments in this discipline.
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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 | 0.334 | 0.275 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.018 | 0.010 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.008 | 0.013 |
| Open science | 0.006 | 0.023 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 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; 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".