One of These Things Is Not Like the Others: The Idea of Precedence in Health Technology Assessment and Coverage Decisions
Bibliographic record
Abstract
Health plans often deliberate covering technologies with challenging purposes, effects, or costs. They must integrate quantitative evidence (e.g., how well a technology works) with qualitative, normative assessments (e.g., whether it works well enough for a worthwhile purpose). Arguments from analogy and precedent help integrate these criteria and establish standards for their policy application. Examples of arguments are described for three technologies (ICSI, genetic tests, and Viagra). Drawing lessons from law, ethics, philosophy, and the social sciences, a framework is developed for case-based evaluation of new technologies. The decision-making cycle includes (1) taking stock of past decisions and formulating precedents, (2) deciding new cases, and (3) assimilating decisions into the case history and evaluation framework. Each stage requires distinctive decision maker roles, information, and methods.
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 | 0.054 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.003 | 0.030 |
| Scholarly communication | 0.010 | 0.028 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.008 | 0.016 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".