Bibliographic record
Abstract
OBJECTIVE: Health technology assessment (HTA) can be used both to promote access to safe, efficacious, and cost-effective technologies, and to discourage access to undesirable ones. Yet HTA has had less success than might be hoped in pursuing the latter goal. This paper examines the scope of HTA as currently practiced to contribute to regulation of access to undesirable technologies. DESIGN: The study design is a critical analysis of HTA's methods, based on an exposition of the normative issues involved in restriction of access to health technologies. The paper classifies technologies that might figure as potential candidates for exclusion into five categories and underscores the key social and ethical dilemmas associated with limiting their use. RESULTS: For four of the five categories of technology outlined, limitation of access necessarily involves denial of benefit. Limitation of access thus inevitably raises difficult normative issues. We show that these are ill-addressed by the range of "evidence" typically considered in technology assessments, which centers predominantly on clinical and technical features such as efficacy, safety, and costs. CONCLUSIONS: If HTA is to enhance our ability to make reasonable decisions concerning the use and diffusion of health technologies, it must better integrate consideration of the social, political, and ethical dimensions of health technologies into the process of technology assessment. We suggest a framework within which to approach this goal.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.042 | 0.009 |
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".