A Framework for Aiding the Translation of Scientific Evidence into Policy: The Experience of a Hospital-Based Technology Assessment Unit
Bibliographic record
Abstract
OBJECTIVES: Very few practical frameworks exist to guide the formulation of recommendations at hospital-based health technology assessment (HTA) units. The objectives of our study were: (i) to identify decision criteria specific to the context of hospital-based health technologies and interventions, (ii) to estimate the extent to which the expert community agrees on the importance of the identified criteria, (iii) to incorporate the identified criteria into a decision-aid tool, and (iv) to illustrate the application of a prototype decision-aid tool. METHODS: Relevant decision criteria were identified using existing frameworks for HTA recommendations, our past experience, a literature search, and feedback from a survey of diverse stakeholders. RESULTS: Based on the survey results, twenty-three decision criteria were incorporated into the final framework. We defined an approach that eschewed a scoring system, but instead relied on a visual means for arriving at a final recommendation, by juxtaposing the importance rating for each criterion against the results of the health technology assessment. For a technology to be approved, a majority of criteria considered important should also have received favorable findings. CONCLUSIONS: We created a simple and practical decision-aid tool that incorporates all decision criteria relevant to a hospital-based HTA unit. With its ease of use and accessibility, our tool renders the subjective decision-making process more structured and transparent.
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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 | 0.503 | 0.433 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.023 | 0.015 |
| Science and technology studies | 0.013 | 0.024 |
| Scholarly communication | 0.040 | 0.026 |
| Open science | 0.009 | 0.027 |
| Research integrity | 0.018 | 0.023 |
| 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; 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".