PD146 Can We Properly Evaluate Genetic And Genomic Applications? A Systematic Review Of Health Technology Assessment Reports
Bibliographic record
Abstract
Introduction The last decade has witnessed a steady adoption of personalized medicine. However, the evaluation of genetic and genomic tests is not straightforward. The purpose of this systematic review was to identify health technology assessment (HTA) reports assessing genetic and genomic tests to summarize the methodologies used, the maturity level of the evidence included, and the highlighted research gaps. Methods The PubMed, Scopus, and Web of Science databases were searched for HTA reports of genetic or genomic tests. The main national and international HTA report repositories (e.g., the international HTA database) were also searched. HTA reports that were specifically created to assess genetic or genomic technologies and included at least three core evaluation components (analytic validity, clinical validity, clinical utility, economic evaluation, organizational aspects, or ethical, legal, and social implications) were included. This study was supported by the European Commission and the Ministry for Universities and Research under the National Recovery and Resilience Plan (M4C2-I1.3 Project PE_00000019 “HEAL ITALIA”). Results Overall, 27,331 unique records were retrieved, 55 of which were included in the systematic review. The reports were mainly from Australia (29%), Canada (27%), and the UK (25%); focused on pharmacogenomics (36%) and oncology (35%); and investigated test use for treatment guidance (42%) or diagnosis (29%). The most reported evaluation components were economic evaluation (87%), clinical utility (76%), and clinical validity (67%). On the other hand, personal utility (7%), patients’ perspectives (27%), and ethical (15%), legal (11%), and social (24%) implications were poorly represented. Analytical validity, safety, and organizational aspects were included in about half of the reports. Conclusions Although these are only preliminary results, the substantial lack of a shared standard in the evaluation of genetic and genomic applications is clear given the heterogeneity of the dimensions addressed among the reports. Theres is a need to strengthen evaluation of the neglected dimensions, which are often of primary importance in defining the value and risks of personalized medicine.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".