Conference 2018: Translating Innovative Technology to Patient Care. An international symposium held jointly by CSPS, CSPT, and CC-CRS, May 22-25, 2018, Toronto, ON, Canada
Bibliographic record
Abstract
Plenaries and Special Presentations:- Mary Haak-Frendscho, Blueline Bioscience & Versant Ventures: The View From Here: Innovation Canadian Style- David Juurlink, Sunnybrook Health Sciences Centre: The North American Opioid Crisis from 30,000 Feet- Tak Mak, Ontario Cancer Institute, Princess Margaret Hospital, Toronto: The Fourth Pillar of Cancer Treatment: It Takes a Village - Richard Weinshilboum, Mayo Clinic: Pharmacogenomics: Clinical Implementation and Future Challenges- Gordon Amidon, University of Michigan: Don’t Throw the BA/BE out with the Bathwater: (Mechanistic Oral BE) CSPS Lifetime Achievement Award: Presentation & Lecture.Conference Sessions:Special Session: Innovation and Management of Modern Pharmaceuticals1. Opioid Crisis2. Regulatory Reforms3. Crossing Biological Membranes4. Knowledge Translation - From Real World Evidence to Canadian Healthcare Needs5. Pharmaceutical Potential of Stem Cell & CRISPR-Mediated Gene Modifications6. Immuno-Oncology7. The Gut Microbiome as a Novel herapeutic Target8. Practical Pharmacology: Case Studies9. Translational Medicine10. Cannabinoids11. Innovative Biomaterials for Drug Delivery12. Pharmacogenomic Implementation13. Drug Therapy in Children
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.014 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.125 | 0.022 |
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".