Priority setting for health technology assessments: A systematic review of current practical approaches
Bibliographic record
Abstract
OBJECTIVES: This study sought to identify and compare various practical and current approaches of health technology assessment (HTA) priority setting. METHODS: A literature search was performed across PubMed, MEDLINE, EMBASE, BIOSIS, and Cochrane. Given an earlier review conducted by European agencies (EUR-ASSESS project), the search was limited to literature indexed from 1996 onward. We also searched Web sites of HTA agencies as well as HTAi and ISTAHC conference abstracts. Agency representatives were contacted for information about their priority-setting processes. Reports on practical approaches selected through these sources were identified independently by two reviewers. RESULTS: A total of twelve current priority-setting frameworks from eleven agencies were identified. Ten countries were represented: Canada, Denmark, England, Hungary, Israel, Scotland, Spain, Sweden, The Netherlands, and United States. Fifty-nine unique HTA priority-setting criteria were divided into eleven categories (alternatives; budget impact; clinical impact; controversial nature of proposed technology; disease burden; economic impact; ethical, legal, or psychosocial implications; evidence; interest; timeliness of review; variation in rates of use). Differences across HTA agencies were found regarding procedures for categorizing, scoring, and weighing of policy criteria. CONCLUSIONS: Variability exists in the methods for priority setting of health technology assessment across HTA agencies. Quantitative rating methods and consideration of cost benefit for priority setting were seldom used. These study results will assist HTA agencies that are re-visiting or developing their prioritization methods.
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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 | 0.199 | 0.428 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.012 | 0.014 |
| Bibliometrics | 0.057 | 0.049 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.012 | 0.014 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.004 | 0.005 |
| 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; 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".