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Record W2094834705 · doi:10.1017/s0266462309090448

A history of health technology assessment at the European level

2009· article· en· W2094834705 on OpenAlexaff
David Banta, Finn Børlum Kristensen, Egon Jonsson

Bibliographic record

VenueInternational Journal of Technology Assessment in Health Care · 2009
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealth Systems, Economic Evaluations, Quality of Life
Canadian institutionsInstitute of Health EconomicsUniversity of CalgaryUniversity of Alberta
Fundersnot available
KeywordsEuropean unionEuropean commissionPolitical scienceHealth technologyPublic administrationEconomic growthCommissionBusinessEconomic policyHealth careEconomics

Abstract

fetched live from OpenAlex

This study summarizes the experience with health technology assessment (HTA) at the European level. Geographically, Europe includes approximately fifty countries with a total of approximately 730 million people. Politically, twenty-seven of these countries (500 million people) have come together in the European Union. The executive branch of the European Union is named the European Commission, which supports several activities, including research, all over Europe and in many other parts of the world. The European Commission has promoted HTA by several policy positions and has funded a series of projects aimed at strengthening HTA in Europe. Around fifteen of the European countries now have formal national programs on HTA and some also have regional public programs. All countries that are members of the European Union and do not have a national approach to HTA have an interest in becoming more involved. The HTA projects sponsored by the European Commission have focused on networking and collaboration among established agencies and institutions for HTA, however, also on capacity building, support, and facilitation in creating mechanisms for HTA in European countries that still do not have any program in the field.

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)0.018
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.902
Threshold uncertainty score0.989

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0180.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.275
GPT teacher head0.486
Teacher spread0.211 · 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; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations62
Published2009
Admission routes1
Has abstractyes

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