Early economic evaluation of emerging health technologies: protocol of a systematic review
Bibliographic record
Abstract
BACKGROUND: The concept of early health technology assessment, discussed well over a decade, has now been collaboratively implemented by industry, government, and academia to select and expedite the development of emerging technologies that may address the needs of patients and health systems. Early economic evaluation is essential to assess the value of emerging technologies, but empirical data to inform the current practice of early evaluation is limited. We propose a systematic review of early economic evaluation studies in order to better understand the current practice. METHODS/DESIGN: This protocol describes a systematic review of economic evaluation studies of regulated health technologies in which the evaluation is conducted prior to regulatory approval and when the technology effectiveness is not well established. Included studies must report an economic evaluation, defined as the comparative analysis of alternatives with respect to their associated costs and health consequences, and must evaluate some regulated health technology such as pharmaceuticals, biologics, high-risk medical devices, or biomarkers. We will conduct the literature search on multiple databases, including MEDLINE, EMBASE, the Centre for Reviews and Dissemination Databases, and EconLit. Additional citations will be identified via scanning reference lists and author searching. We suspect that many early economic evaluation studies are unpublished, especially those conducted for internal use only. Additionally, we use a chain-referral sampling approach to identify authors of unpublished studies who work in technology discovery and development, starting out with our contact lists and authors who published relevant studies. Citation screening and full-text review will be conducted by pairs of reviewers. Abstracted data will include those related to the decision context and decision problem of the early evaluation, evaluation methods (e.g., data sources, methods, and assumptions used to identify, measure, and value the likely effectiveness and the costs and consequences of the new technology, handling of uncertainty), and whether the study results adequately address the main study question or objective. Data will be summarized overall and stratified by publication status. DISCUSSION: This study is timely to inform early economic evaluation practice, given the international trend in early health technology assessment initiatives.
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 | 0.171 | 0.224 |
| Meta-epidemiology (narrow) | 0.008 | 0.008 |
| Meta-epidemiology (broad) | 0.020 | 0.018 |
| Bibliometrics | 0.016 | 0.017 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.007 | 0.006 |
| Research integrity | 0.012 | 0.013 |
| Insufficient payload (model declined to judge) | 0.070 | 0.013 |
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".