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Record W4410941305 · doi:10.1017/s0266462325100123

Defining early health technology assessment: building consensus using Delphi technique

2025· review· en· W4410941305 on OpenAlexafffund
Janneke P.C. Grutters, Janet Bouttell, Payam Abrishami, Sofia Ahmed, Amanda Cole, Dalia Dawoud, Carla Fernández-Barceló, Geert Frederix, Neil Hawkins, Jonathan Karnon, Sharon Leadbitter, Christopher McCabe, Jani Mueller, Samuel Owusu Achiaw, Andrew Partington, Laura Sampietro-Colom, Abril Seyahian, Rabia Sucu, Michelle Tew, Sasha van Katwyk, Yves Verboven, Yi Wang

Bibliographic record

VenueInternational Journal of Technology Assessment in Health Care · 2025
Typereview
Languageen
FieldSocial Sciences
TopicDelphi Technique in Research
Canadian institutionsInstitute of Health Economics
FundersHorizon 2020 Framework ProgrammeHealth Technology Assessment internationalEuropean Commission
KeywordsDelphiDelphi methodConsensus conferenceHealth technologyManagement sciencePolitical scienceMedicineComputer scienceEngineeringLibrary scienceHealth careArtificial intelligenceLaw

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Bibliometrics, Research integrity
Consensus categoriesResearch integrity
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.891
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0140.004
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0040.001
Research integrity0.0020.006
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.122
GPT teacher head0.595
Teacher spread0.473 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations20
Published2025
Admission routes2
Has abstractyes

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