Challenges in health technology assessments of genetic tests
Bibliographic record
Abstract
Abstract: In the past decade, the use of genetic tests has grown rapidly worldwide. These technologies are often used in prenatal screening, carrier testing, prognostic testing, and diagnostic testing. Standard methods for meta-analysis of diagnostic test accuracy and cost-effectiveness analysis may need adaptation for health technology assessments (HTAs) of genetic tests. We provide some considerations relevant to these evaluations. We briefly address the following challenges for HTAs of genetic tests: (I) performing Bayesian meta-analysis of diagnostic test accuracy for rare genetic conditions; (II) performing Bayesian meta-analysis of diagnostic test accuracy in the absence of a perfect reference standard; (III) constructing economic models that account for conditional dependence between tests in the absence of a perfect reference standard; (IV) ascertaining the true prevalence of genetic conditions for economic analyses; (V) defining the time horizon and specifying health outcomes for economic modelling; and (VI) cost item measurement and valuation.
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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 | 0.431 | 0.706 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.013 | 0.016 |
| Open science | 0.009 | 0.008 |
| Research integrity | 0.008 | 0.012 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".