Connecting minds and catalyzing collaboration: the interest groups of health technology assessment international
Bibliographic record
Abstract
Health Technology Assessment international (HTAi) supports global collaboration and innovation in HTA through its dynamic network of Interest Groups (IGs). These thematic communities provide a dedicated platform for members to engage in focused, collaborative efforts that drive professional exchange, advance methodologies, and develop best practices in HTA. This commentary offers a panoramic overview of all IGs, their evolution, aim, and initiatives. By drawing on diverse stakeholder perspectives, spanning academia, clinical practice, industry, and patient communities, the IGs foster inclusiveness and extend HTAi's influence to significantly contribute to the broader HTA community. Through activities such as workshops, conference sessions, webinars, publications, and research projects, they offer opportunities for professional development and thought leadership. The IGs' cross-cutting contributions position them as engines of innovation to ensure HTAi remains at the forefront of shaping a globally relevant, responsive, and ethically grounded HTA ecosystem.
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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 | 0.036 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.011 |
| Scholarly communication | 0.009 | 0.014 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".