Does the public think it is reasonable to wait for more evidence before funding innovative health technologies? The case of PET scanning in Ontario
Bibliographic record
Abstract
OBJECTIVES: Many innovative health technologies do not have a sufficient evidence-base to allow for adequate assessment of their benefits. Funders in several countries have been exploring arrangements that allow for temporary or partial coverage of these technologies, but only as part of a further evaluation. The public's support of arrangements that restrict access to innovative technology until sufficient evidence is available is crucial if these arrangements are going to remain viable. The project's other objective is to examine the lay public's views on a case in which patients' publicly funded access to an innovative health technology is being delayed until there is sufficient evidence to justify a coverage decision. The case considered is the Ontario (Canada) government's decision to restrict access to positron emission tomography (PET) scans until further evidence becomes available. METHODS: The case was deliberated on by twenty-six members of the Toronto Health Policy Citizens' Council, with a follow-up survey administered to individual council members. RESULTS: The majority of council members agreed that the approach taken by the government was reasonable and in the best interests of its citizens. The council did express concerns regarding certain aspects of the case, including about the length of time it is taking to obtain further evidence. CONCLUSIONS: Public support for arrangements that limit access to new technologies will likely vary depending on the details of the specific arrangement being proposed. Deliberative public dialogue can be effectively used to identify cases the general public is most likely to support.
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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 | 0.028 | 0.067 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.033 | 0.022 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.016 | 0.010 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".