Evaluating Genetic Tests: A Systematic Review and Critical Appraisal of Assessment Frameworks
Bibliographic record
Abstract
Abstract Background The evaluation of genetic/genomic tests has traditionally relied on ad hoc methods, often centered on technical criteria and predominantly influenced by the ACCE framework. While these approaches cover analytical and clinical aspects, they frequently neglect broader contextual factors. Although some frameworks grounded in Health Technology Assessment (HTA) offer a more comprehensive perspective, they remain largely underutilized. This review aims to identify existing evaluation frameworks for genetic/genomic tests and summarize their main characteristics. Methods Searches were conducted in PubMed, Scopus, Web of Science, Google Scholar, and Google Search for articles describing original assessment frameworks specifically designed for genetic/genomic tests. Data on assessment components was extracted, and specific assessment issues from the EUnetHTA HTA core model were linked to components. This study is supported by the EC and MUR under PNRR - M4C2-I1.3 Project PE_00000019 ‘HEAL ITALIA’. Results 12546 unique records were screened, and 29 studies were included, reporting 24 different frameworks. The frameworks, published between 2000 and 2019, primarily from the USA, Canada, and the UK, focused on clinical value and economic aspects, consistently considering technical, ethical, legal, and social aspects. However, there was limited attention to non-health outcomes and to organizational, educational, and implementation challenges. Only one out 24 frameworks considered all the assessment components. Finally, all the extracted components matched with at least one or multiple issues from the HTA core model. Conclusions evaluation frameworks for genetic/genomic tests are numerous but fragmented: most emphasize clinical aspects, while largely ignoring non-health outcomes and organizational and implementation aspects. Of 24 frameworks, only one addressed every assessment component. Nevertheless, all components mapped to issues in the EUnetHTA Core Model. Key messages • There is an urgent need for a universally accepted framework to evaluate genetic and genomic tests. • One promising approach is to apply a general HTA methodology, potentially grounded in the EUnetHTA HTA core model, that incorporates robust theoretical and methodological foundations.
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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 | 0.235 | 0.491 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.014 | 0.014 |
| Bibliometrics | 0.057 | 0.032 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.007 | 0.008 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".