Is genomic testing properly evaluated? A systematic review of health technology assessment reports
Bibliographic record
Abstract
Abstract Background The last decade has witnessed a steady embrace of Personalized Medicine. However, the evaluation of genetic/genomic tests is not straightforward. The purpose of this systematic review is to identify health technology assessment (HTA) reports assessing genetic and genomic tests, to summarize the methodologies used, the maturity level of the evidence included in it and the highlighted gaps in research. Methods PubMed, Scopus, and Web of Science were searched. Additionally, a desk research was performed on the main HTA reports repositories. HTA reports expressly created to assess genetic/genomic technologies including at least three core evaluation components (analytic validity, clinical validity, clinical utility, economic evaluation, organizational aspects, ethical, legal, and social implications) were included. This study was supported by the EC and MUR under PNRR - M4C2-I1.3 Project PE_00000019 ‘HEAL ITALIA’. Results Overall, 27331 unique records were retrieved, 55 of which were included in the systematic review. The reports were mainly from Australia (29%), Canada (27%) and UK (25%), regarded pharmacogenomics (36%) and oncology (35%), and analysed test use for treatment choice (29%) and diagnosis (13%). The most reported evaluation components were economic evaluation (87%), clinical utility (76%), and clinical validity (67%). Personal utility (7%), ethical (15%), legal (11%) and social (24%) implications and the patient’s perspective (27%) were poorly represented. Analytical validity, safety, and organizational aspects were included in about half of the reports. Discussion Although these are only preliminary results, the substantial lack of a shared standard in the evaluation of genetic/genomic applications appears clear, given the heterogeneity of the dimensions addressed between reports, as well as the need to strengthen the evaluation of the neglected dimensions, often of primary importance in the definition of the value and risks of personalized medicine. Key messages • The findings highlight the lack of a standard in evaluating genetic/genomic applications. • A common methodology should be developed to comprehensively evaluate genetic/genomic applications.
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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 | 0.383 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.012 | 0.008 |
| Bibliometrics | 0.031 | 0.027 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".