Evaluating Genetic Testing: A Systematic Review of Assessment Frameworks
Bibliographic record
Abstract
Abstract Introduction The assessment of the risks and benefits of genetic/genomic tests has long been addressed using various frameworks. They are mostly ACCE-based, focus on technical aspects, but often overlook economic and organizational considerations. The few HTA-based approaches, though more comprehensive, lack validation and implementation. This review’s purpose is to identify all evaluation frameworks for genetic/genomic tests and synthesize their key aspects. Methods PubMed, Scopus, Web of Science, and Google Scholar were searched. Inclusion criteria were documents describing evaluation frameworks for genetic/genomic tests, that were original, specifically created, and covering at least three assessment domains. This study was supported by the EC and MUR under PNRR - M4C2-I1.3 Project PE_00000019 ‘HEAL ITALIA’. Results Overall, 12546 unique records were screened, of which 67 documents were assessed for eligibility. A total of 29 studies were included, reporting 24 different frameworks. These frameworks were published between 2000 and 2019, mostly from USA (50%), Canada (13%) and UK (13%). There was substantial interest in the economic facets of the technology (92%), albeit without extensive detail, and high attention was given to its technical accuracy (70-90%). The clinical value was also consistently mentioned (70-90%), similarly to legal, ethical, and social considerations (70-80%). However, there was minimal emphasis on non-health outcomes (20-50%), and insufficient attention to organizational, educational, and implementation barriers (20-50%). Discussion A pressing need exists for a universally accepted evaluation framework for genetic/genomic tests. Applying a general HTA methodology, potentially based on the EUnetHTA core model, that can integrate solid theoretical and methodological principles, and result in a validated, comprehensive, and widely shared tool for genetic test evaluation, is a viable option to foster the implementation of these technologies. Key messages • Genetic/genomic test evaluations focus on technical accuracy and clinical value, often missing economic and organizational aspects. • A universal, validated, HTA-based evaluation framework for genetic/genomic tests is needed to enhance their implementation in clinical practice.
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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 | 0.100 | 0.290 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.012 |
| Bibliometrics | 0.042 | 0.032 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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".