Editorial [Personalized Medicine Beyond Genomics: New Technologies, Global Health Diplomacy and Anticipatory Governance]
Bibliographic record
Abstract
Genomics is one of the key technologies enabling personalized medicine and the broader field of theragnostics (i.e., the fusion of therapeutics and diagnostic medicine). Yet other high-throughput technologies (e.g., nanotechnology and proteomics) are also rapidly emerging on the horizon in the postgenomics era since the completion of the Human Genome Project in 2003. Applications of these health technologies, too, are being diversified in personalized medicine. These include both “old” and “new” applications aimed at better understanding host-environment interactions, for example, pharmacogenomics, nutrigenomics (featured in the June and September 2009 issues of the CPPM) and pharmacoproteomics, to name a few. Importantly, all these advances are now taking place both “in” and “outside” the traditional laboratory space as personalized medicine innovations diffuse, albeit slowly, from upstream discovery oriented applications (e.g., search for genes associated with common complex diseases) to downstream health products, diagnostics, and personalized interventions in the clinic [2], although not always in that linear direction [3]. Personalized medicine in the postgenomics era calls for a transdisciplinary approach [4], and considerations for how best to develop innovation frameworks to support safe and effective deployment of the new enabling diagnostic technologies. CPPM aims to address the previously unmet needs in both pharmacogenomics and personalized medicine, for example, by moving beyond the artificial compartmentalization of biomarkers and knowledge across health technologies and disciplinary silos. This is crucial as there are important lessons to be learned from different personalized health interventions, whether they involve pharmaceuticals, nutrition, stem cell therapy, or are enabled by genomics, proteomics and nanotechnology. Indeed, these health technologies and their applications can usefully cross-inform each other and thereby help strengthen and triangulate the attendant evidentiary base for personalized medicine. This integrative vision of personalized medicine that includes and extends beyond pharmacogenomics is now being put into practice by the CPPM through vigilant and transdisciplinary horizon scanning, and rigorous peer-review with strong international outreach to expertise available in different global regions. Hence, the December issue of the Journal features two new health technologies - nanotechnology and proteomics - that are already beginning to impact the individualization of drug therapy.
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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 | 0.005 | 0.030 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.024 | 0.029 |
| Insufficient payload (model declined to judge) | 0.015 | 0.011 |
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".