PP133 What Services And Products Should A Health Technology Assessment Agency Provide?
Bibliographic record
Abstract
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.
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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 | 0.017 | 0.097 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.011 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.062 | 0.012 |
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.
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".