Theranostics in primary care: pharmacogenomics tests and beyond
Bibliographic record
Abstract
Theranostics represents a broadening in the scope of personalized medicine to include companion diagnostics for health interventions ranging from drugs to vaccines, as well as individual susceptibility to disease. Surprisingly, in the course of this broadening of personalized medicine discourse, relatively little attention has been paid to primary care (as compared with tertiary healthcare settings) despite its vast patient population and being a crucial entry point to health services. Recent advances in pharmacogenomics (PGx), a classical theranostics application whereby genotyping and/or gene expression-based tests are used for targeted or optimal therapy, revealed new opportunities to characterize more precisely human genomic variation and the ways in which it contributes to person-to-person and population variations in drug response. In the immediate foreseeable future, the primary-care physicians are expected to play an ever increasing crucial role in PGx-based prescribing in order to reduce the rates of adverse drug events and improve drug efficacy, yet PGx testing in primary care remains limited. In this article, the authors review the advances in PGx applications, the barriers for their adoption in the clinic from a primary care point of view and the efforts that are being undertaken to move PGx forward in this hitherto neglected application context of theranostic medicine. Finally, the authors propose several salient recommendations, including a 5-year forecast, to accelerate the current convergence between PGx and primary care.
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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 | 0.017 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".