Personalized medicine policy challenges: measuring clinical utility at point of care
Bibliographic record
Abstract
Pharmacogenomics, driven by advances in genomics, helps to explain patients' individual variability in response to therapies. Personalized medicine, the application of the increasing understanding of pharmacogenomics, and information technology are intertwined from discovery to delivery at point of care, through to tracking clinical outcomes. Although exemplary cases of personalized medicine adoption demonstrate patient benefit and cost-effectiveness, a remaining barrier to large-scale real-world uptake of this novel approach in medicine is policy change. At point-of-care implementation, case studies will need to measure personalized medicine application outcomes of relevance to policy-makers and as evidence of clinical utility. Assessments need to be consistent across case studies. Standardizing specifications for case studies will better inform policy-makers performing economic evaluations on the use of personalized medicine.
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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 | 0.124 | 0.250 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.002 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 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; 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".