Health technology assessment: A comprehensive framework for evidence-based recommendations in Ontario
Bibliographic record
Abstract
OBJECTIVES: This study describes the development of a framework for health technology decisions, for Ontario Health Technology Advisory Committee (OHTAC) in Ontario, Canada. METHODS: OHTAC convened a "Decision Determinants Sub-Committee" in January 2007, which undertook a systematic literature review and conducted key informant interviews to develop an explicit decision-making framework. RESULTS: The "Decision Determinants Sub-Committee" offered recommendations about decision criteria, and the process by which decisions are made. Decision criteria include (i) overall clinical benefit, (ii) consistency with societal and ethical values, (iii) value for money, and (iv) feasibility of adoption into the health system. The decision process should be transparent and fair and should use a deliberative process in delivering recommendations. CONCLUSIONS: This methodology is currently being pilot tested in a live environment: OHTAC. It will be evaluated and revised according to its feasibility, acceptability, and perceived usefulness.
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 | 0.198 | 0.218 |
| Meta-epidemiology (narrow) | 0.005 | 0.004 |
| Meta-epidemiology (broad) | 0.009 | 0.008 |
| Bibliometrics | 0.035 | 0.029 |
| Science and technology studies | 0.008 | 0.011 |
| Scholarly communication | 0.024 | 0.012 |
| Open science | 0.011 | 0.015 |
| Research integrity | 0.012 | 0.012 |
| Insufficient payload (model declined to judge) | 0.006 | 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; 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".