Conference 2016: From Drug Discovery to Health Outcomes: Population to Patient. An international symposium held jointly by CSPS and CC-CRS, May 31-June 3, 2016, Vancouver, BC, Canada
Bibliographic record
Abstract
Plenaries and Special Presentations:Carolyn Buser-Doepner, GSK: "New Trends in Pharma-Academia Collaborations for Drug Discovery"Chris Halyk, President, Janssen Inc.: "Are Innovative Medicines and our Life Sciences Industry at Risk in Canada?"Aaron Schimmer, Princess Margaret Cancer Centre: "New Therapeutic Strategies to Target the Mitochondria in Leukemia"Ivana Cecic, Genome BC: "Genomics in Canada: From Knowledge Generation to Patient Outcomes"Adam Rosebrock, University of Toronto: "Quantitative Mass-Spectrometry Metabolomics for Direct Biochemical Phenotyping"Fakhreddin Jamali, University of Alberta: CSPS Lifetime Achievement Award Lecture - "Pharmaceutical Research and Development, Lessons Learned"Conference Sessions:Special Session: Innovation and Management of Modern Pharmaceuticals1. Special Populations2. Nanomedicines Become Personal: Opportunities and Challenges3. Mucosal Drug Delivery4. Broaching the Fourth Hurdle: Getting Drugs on the Formulary5. Pharmacogenomics in the Clinic and Community6. Responsive Drug Delivery Systems7. Drug Targeting and Targeting Drugs8. Health Sustainability Evidence9. Integrating Pharmaceutical Sciences into a Pharm D Curriculum10. Analytical Innovation to Support Precision Medicine and Biologicals Development11. Protein and Peptide Delivery
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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 | 0.015 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.068 | 0.012 |
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".