TIP-13. Advancing Patient-Centered Neurological Trials in Canada through Scientific Innovation and Operational Excellence at The Clinical Research Unit at The Montreal Neurological Institute and Hospital
Bibliographic record
Abstract
Abstract The Clinical Research Unit (CRU) at the Montreal Neurological Institute-Hospital (MNI) represents Canada’s most comprehensive academic center for neurological clinical trials, offering industry partners unmatched scientific capabilities and operational efficiencies. With 40 years of experience, the CRU currently manages 115+ active trials across all phases (I-IV), including 12 gene therapy trials and 18 studies in rare neurological diseases. BACKGROUND Our center provides direct access to specialized patient populations (35000+ MNI outpatients) through dedicated clinics serving 1100+ participants annually, including the largest brain tumour clinic in Quebec (2000+ patients, 350+ new cases/year). METHODS Our infrastructure includes Canada’s only 7T MRI dedicated to clinical trials, a 3T MRI, high-resolution PET and micro-PET, a cyclotron, MEG, real-time EEG, a GMP-certified clean room for cell and gene therapies, interventional radiology expertise, dedicated lab infrastructure for clinical trials, and Phase I overnight facilities. The CRU team includes 50 full-time staff: ICU-trained nurses, 20 certified research coordinators, and dedicated ethics, finance, and pharmacy professionals. The CRU is known for its expertise in rare diseases and is home to a first-of-its-kind Phase 1 Unit dedicated to neurological disorders. Operational metrics demonstrate our efficiency thanks to a Neuro-specialized Review Ethics Board, CTMS-enabled operations, membership of Catalis, Quebec’s Fast Track Network (reducing ethics approval timelines by 60%). Moreover, conducting both industry-sponsored and investigator-initiated trials, with a high patient retention rate. CONCLUSIONS With 15 active industry partnerships and 5 ongoing open science collaborations, the CRU has contributed to 7 FDA/Health Canada approvals since 2018. For sponsors seeking excellence in CNS trials, we offer: (1) Canada’s largest neuroscience patient population with deep phenotyping data, (2) specialized infrastructure for advanced therapies, (3) specialized personnel and infrastructure, and (4) world-leading clinician-scientists with academic leadership in trial design. Our track record makes us the ideal partner for innovative neurological drug 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 | 0.082 | 0.080 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.012 | 0.003 |
| Open science | 0.007 | 0.012 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.024 | 0.005 |
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".