The Health Economics of Genomic Technologies: A Growing Evidence Base on Value
Bibliographic record
Abstract
The sequencing of the first human genome in 2003 catalysed development of the field of precision medicine over the past two decades. Next-generation sequencing approaches developed in the clinical research setting are now emerging into clinical practice. These approaches allow either the whole genome or key sections of the genome (exome sequencing or targeted panel testing) to be sequenced [ 1 ]. This genomic information can be generated at speed, and increasingly at a reasonable cost, although bioinformatics necessary to interpret sequence data and subsequent treatment costs can remain prohibitively expensive. While potentially costly, genomic information can guide diagnosis and clinical management for patients with cancer, rare diseases and chronic diseases, potentially improving health and well-being outcomes for patients and families. Large-scale genome sequencing projects, such as the 100,000 Genomes Project in England, Australian Genomics and the Canadian Precision Health Initiative continue to provide insights that not only impact current clinical management for patients, but also inform the development of new genomic and non-genomic interventions and therapies [ 2 , 3 , 4 ].
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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 | 0.024 | 0.129 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.021 | 0.031 |
| Insufficient payload (model declined to judge) | 0.014 | 0.005 |
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".