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Record W4389704605 · doi:10.1017/s0266462323002453

PP133 What Services And Products Should A Health Technology Assessment Agency Provide?

2023· article· en· W4389704605 on OpenAlexaboutno aff
Maria-Jose Faraldo-Valles, Maria-Carmen Maceira-Rozas, Beatriz Casal Acción, Patricia Gomez, Yolanda Triñanes

Bibliographic record

VenueInternational Journal of Technology Assessment in Health Care · 2023
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealth Systems, Economic Evaluations, Quality of Life
Canadian institutionsnot available
Fundersnot available
KeywordsHealth technologyTransparency (behavior)TimelineAgency (philosophy)BusinessHealth carePortfolioNiceMedicinePolitical scienceComputer scienceFinanceSociologyGeography

Abstract

fetched live from OpenAlex

Introduction Health technology assessment (HTA) bodies support healthcare decision-making by producing different kind of products. The high speed of the healthcare innovations and the scenarios such as the COVID-19 pandemic challenge HTA organizations to adapt their services to better respond to these demands. The Spanish Network of HTA Agencies (RedETS) is redefining the services and the products in its portfolio. The first step has been conducting a review in order to identify the most relevant HTA products. Methods A scoping review with two sections was conducted: (i) analysis of results from a bibliographic search performed in the main biomedical databases; and (ii) analysis of results from a manual review of the official websites of seven international HTA agencies: CADTH (Canada), INESSS (Canada), SBU (Sweden), NICE (United Kingdom), IQWIG (Germany), HAS (France), IECS (Argentina) and IETS (Colombia). The EUnetHTA website was also reviewed. Results The search identified 1,311 references; 21 studies were considered relevant. The main topic found was about rapid responses services. The standard timeline for these should be less than six months, with even some produced in days. Transparency about methodology and involvement of decision-makers were considered key points to be included. Website analysis revealed similar HTA reports production but variation in the domains and elements considered. The timeframe for conducting a full HTA report can be up to 24 months, with a median of 12 months. Agencies also offer some kinds of rapid response services. Scientific consultation and horizon scanning systems for emerging technologies are other services performed by some agencies. Conclusions The review reveals that agencies have different products to address different needs throughout the life cycle of technologies: from scientific advice to full HTA. In addition, HTA agencies have incorporated rapid responses into their services. According to literature, these products could support short-term decision-making.

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)0.017
metaresearch head score (Gemma)0.097
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: none
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.062
Threshold uncertainty score0.208

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0170.097
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0060.011
Science and technology studies0.0010.002
Scholarly communication0.0080.010
Open science0.0010.003
Research integrity0.0040.003
Insufficient payload (model declined to judge)0.0620.012

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 head0.226
GPT teacher head0.505
Teacher spread0.279 · 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
GenreCommentary

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".

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Citations1
Published2023
Admission routes1
Has abstractyes

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