Cross-national comparison of technology assessment processes
Bibliographic record
Abstract
OBJECTIVES: To compare methods and results among four health technology assessment organizations in different countries. METHODS: All assessment reports published between 1999 and 2001 by VATAP (United States), NICE (United Kingdom), CCOHTA (Canada), and AETS (Spain), were reviewed. Detailed information about the organization, the technology assessed, the methods used, and the recommendations made were collected. A descriptive analysis of the variables, as well as comparisons of means and proportions, was performed. RESULTS: Sixty-one reports assessing seventy-six technologies were published: nine (11.8 percent) by VATAP, thirty-nine (51.3 percent) by NICE, twenty (26.3 percent) by CCOHTA, and eight (10.5 percent) by AETS. A total of 64.5 percent of the technologies assessed were related to a high prevalence disease in the corresponding country. Most of the assessments addressed treatments (73.7 percent) and were mostly drugs (56.6 percent) and devices (23.7 percent). Most organizations used reviews of effectiveness and economic evaluations (64.5 percent), systematic reviews (21.1 percent), and original economic evaluations (36.7 percent). In 38.1 percent, the technology was recommended; the rest of the cases had no formal recommendations. CONCLUSIONS: Critical issues for future technology assessment efforts are making assessment processes more consistent, transparent, and evidence-based; formalizing the inclusion of economic and ethical considerations; and making more explicit the prioritization process for selecting technologies for assessment and reassessment.
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 | 0.400 | 0.607 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.018 | 0.017 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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; 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".