Ethical challenges for Health Technology Assessment (HTA) in the evolving evidence landscape
Bibliographic record
Abstract
Since its inception, Health Technology Assessment (HTA) has typically determined the value of a technology by collecting information derived from randomized clinical trials (RCTs), in line with the principles of evidence-based medicine (EBM). However, data from RCTs did not constitute the sole source of information, as other types of evidence (such as primary qualitative research) have often been utilized. Recent advances in both generating and collecting other types of evidence are broadening the landscape of evidence, adding complexity to the discussion of "robustness of evidence." What are the consequences of these recent developments for the methodology and conduct of HTA, the HTA community, and its ethical commitments? The aim of this article is to explore some ethical challenges that are emerging in the current evolving evidence landscape, particularly changes in evidence generation and collection (e.g., diversification of data sources), and shifting standards of evidence in the field of HTA (e.g., increasing acceptability of evidence that is thought of as lower quality). Our conclusion is that deciding how to best maintain trustworthiness is common to all these issues.
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 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 | 0.479 | 0.529 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.004 | 0.036 |
| Scholarly communication | 0.016 | 0.021 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.014 | 0.027 |
| Insufficient payload (model declined to judge) | 0.002 | 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".