Integrating Personalized Medicine in the Canadian Environment: Efforts Facilitating Oncology Clinical Research
Bibliographic record
Abstract
There is currently a rapid evolution of clinical practices based on the introduction of patient stratification and molecular diagnosis that is likely to improve health outcomes. Building on a strong research base, complemented by strong support from clinicians and health authorities, the oncology field is at the forefront of this evolution. Yet, clinical research is still facing many challenges that need to be addressed in order to conduct necessary studies and effectively translate medical breakthroughs based on personalized medicine into standards of care. Leveraging its universal health care system and on resources developed to support oncology clinical research, Canada is well positioned to join the international efforts deployed to address these challenges. Available resources include a broad range of structures and funding mechanisms, ranging from direct clinical trial support to post-marketing surveillance. Here, we propose a clinical model for the introduction of innovation for precision medicine in oncology that starts with patients' and clinicians' unmet needs to initiate a cycle of discovery, validation, translation and sustainability development.
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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 | 0.084 | 0.086 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.017 | 0.017 |
| Scholarly communication | 0.017 | 0.006 |
| Open science | 0.004 | 0.022 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.008 | 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".