HEALTH TECHNOLOGY REASSESSMENT: SCOPE, METHODOLOGY, & LANGUAGE
Bibliographic record
Abstract
Health systems are challenged continuously to provide the highest quality universal health care within their means. While for 30 years, health technology assessment (HTA) has contributed to the process of evidence-informed decision making and the managed entry of new technologies, its remit has not expanded to include assessment of technologies currently in use, as a means of managing their use and potentially their exit. We propose that health technology reassessment (HTR) become standard practice, an integral part of all health technology assessment agencies, and that we develop standardized models and methodologies for reassessment drawing from what we have learned from HTA.
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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 | 0.071 | 0.233 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.003 | 0.018 |
| Scholarly communication | 0.018 | 0.012 |
| Open science | 0.007 | 0.003 |
| Research integrity | 0.016 | 0.028 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".