Institutional Profile: The Beaulieu-Saucier Université de Montréal Pharmacogenomics Centre at the Montreal Heart Institute
Bibliographic record
Abstract
The Beaulieu-Saucier Université de Montréal Pharmacogenomics Centre (Québec, Canada) is an academic organization dedicated to advancing knowledge and promoting discoveries in personalized medicine by leading high-quality pharmacogenomic research in partnership with industrial, governmental and academic organizations. Since its establishment in 2008, the Centre has been leading innovative genomics research programs for all phases of drug development and has conducted over 120 pharmacogenomic projects through collaborations with international partners using state-of-the-art platforms with the highest-quality data. The Centre has a fully equipped DNA and genomic laboratory facility, supported by bioinformatics, statistical genetics, quality assurance and project management teams. More recently, the Centre has partnered with the Centre of Excellence in Personalized Medicine for the commercialization of biomarkers and implementation in clinical practice, and with the Montreal Health Innovations Coordinating Centre for study coordination and integration with drug development pipelines. Through its academic excellence, unique expertise in Canada and international industrial partners, the Pharmacogenomics Centre is providing the technologies and research discoveries needed to provide the right therapy to the right patients.
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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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.388 | 0.081 |
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".