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Record W2892063514 · doi:10.3389/fmed.2018.00231

EUPATI Guidance for Patient Involvement in Medicines Research and Development: Health Technology Assessment

2018· article· en· W2892063514 on OpenAlexfundno aff
Amy Hunter, Karen Facey, Victoria Thomas, David Haerry, Kay Warner, Ingrid Klingmann, Matthew M. May, Wolf See

Bibliographic record

VenueFrontiers in Medicine · 2018
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealth Systems, Economic Evaluations, Quality of Life
Canadian institutionsnot available
FundersBundesinstitut für Arzneimittel und MedizinprodukteHealth Technology Assessment internationalPfizerAmgen
KeywordsHealth technologyHealth careMedicineLegislationPolitical scienceBusinessPublic relations

Abstract

fetched live from OpenAlex

The main aim of health technology assessment (HTA) is to inform decision making by health care policy makers. It is a systematic process that evaluates the use of health technologies and generally involves a critical review of international evidence related to clinical effectiveness of the health technology vs. the best standard of care. It can also include an evaluation of cost effectiveness, and social and ethical impacts in the local health care system. The HTA process advises whether or not a health technology should be used, and if so, how it is best used and which patients are most likely to benefit from it. The importance of patient involvement in HTA is becoming widely recognized, for scientific and democratic reasons. The extent of patient involvement in HTA varies considerably across Europe. Commonly HTA is still focused on quantitative evidence to determine clinical and/or cost effectiveness, but the interest in understanding patients' experiences and preferences is increasing. Some HTA bodies provide support for participation in their processes, but again this varies widely across Europe. The involvement of patients in HTA is determined at the national and regional level, and is not subject to any European-wide legislation. The guidance text presented in this article was developed as part of the work of the European Patients' Academy on Therapeutic Innovation (EUPATI) and covers the interaction between HTA bodies and patients and their representatives when medicines are being assessed. Other EUPATI guidance documents relate to patient involvement in pharmaceutical industry-led research and development, ethics committees, and regulatory authorities. The guidance provides recommendations for activities to support patient involvement in HTA bodies and specific guidance for individual HTA processes. It seeks to improve patient involvement, using the outcomes of published research and consensus-building exercises. It also draws on good practice examples from individual HTA bodies. The guidance is not intended to be prescriptive and should be used according to specific circumstances, national legislation, or the unique needs of each interaction. This article represents the formal publication of the HTA guidance text with discussion about recent progress in, and continuing barriers to, patient involvement in HTA.

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 imitation

Not 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.

metaresearch head score (Codex)0.105
metaresearch head score (Gemma)0.212
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.105
Threshold uncertainty score0.554

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1050.212
Meta-epidemiology (narrow)0.0020.003
Meta-epidemiology (broad)0.0030.006
Bibliometrics0.0040.004
Science and technology studies0.0040.006
Scholarly communication0.0110.008
Open science0.0080.011
Research integrity0.0600.018
Insufficient payload (model declined to judge)0.0270.032

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 head0.433
GPT teacher head0.515
Teacher spread0.082 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreMethods

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

Citations72
Published2018
Admission routes1
Has abstractyes

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