Moving beyond the Court of Public Opinion: A Citizens’ Jury Exploring the Public’s Values around Funding Decisions for Ultra-Orphan Drugs
Bibliographic record
Abstract
Health system decision-makers need to understand the value of new technology to make "value for money" decisions. Typically, narrow definitions of value are used. This paper reports on a Canadian Citizens' Jury which was convened to elicit those aspects of value that are important to the public. The criteria used by the public to determine value included those related to the patient, those directly related to caregivers and those directly created for society. Their choices were not binary (e.g., cost vs. health gained), but rather involved multiple factors (e.g., with respect to patient factors: disease severity, health gained with the drug, existence of alternatives, life expectancy, patient age and affordability). Overall, Jurors prioritized funding treatments for ultra-rare disease populations when the treatment offered significant improvements in health and quality of life, and when the pre-treatment health state was considered extremely poor. The prevalence of the disease by itself was not a factor in the choices. Some of the findings differ from previous work, which use survey methods. In our Citizens' Jury, Jurors were able to become more familiar with the question at hand and were exposed to a broad and balanced collection of viewpoints before and throughout engaging in the exercises. This deliberative approach allows for a more nuanced approach to understanding value.
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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 | 0.150 | 0.219 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.095 | 0.045 |
| Scholarly communication | 0.033 | 0.014 |
| Open science | 0.006 | 0.017 |
| Research integrity | 0.032 | 0.037 |
| Insufficient payload (model declined to judge) | 0.005 | 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".