EARLY EVALUATION OF NEW HEALTH TECHNOLOGIES: THE CASE FOR PREMARKET STUDIES THAT HARMONIZE REGULATORY AND COVERAGE PERSPECTIVES
Bibliographic record
Abstract
With an increasing awareness that active engagement between policy decision makers, HTA agencies, regulators and payers with industry in the premarket space is needed, a disruptive comprehensive approach is described which moves the evidentiary process exclusively into this space. Single harmonized studies pre-market to address regulatory and coverage needs and expectations are more likely to be efficient and less costly and position evidence to drive rather than test innovation. An example of such a process through the MaRS EXCITE program in Ontario, Canada, now undergoing proof of concept, is briefly discussed. Other examples of dialogue between decision makers and industry pre-market are provided though these are less robust than a comprehensive evidentiary approach.
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 | 0.720 | 0.653 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.006 | 0.025 |
| Scholarly communication | 0.038 | 0.058 |
| Open science | 0.012 | 0.031 |
| Research integrity | 0.030 | 0.035 |
| Insufficient payload (model declined to judge) | 0.006 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".