Nicolas Garel: Combining psychoactive molecules and psychotherapy for patients suffering from mood and substance use disorders: a new therapeutic paradigm
Bibliographic record
Abstract
Dr Nicolas Garel is an Assistant Professor in the Department of Psychiatry and Addictology at the University of Montreal and a junior investigator at the Research Center of the Centre Hospitalier de l'Université de Montréal. Dr Garel did his psychiatry residency at McGill University before completing the Clinician-Investigator Program and a Master's degree at McGill University, studying the potential role of ketamine in the discontinuation of benzodiazepines and related drugs. Dr Garel then completed a clinical fellowship in Addiction Medicine at Stanford University. His research program focuses on the integration of psychoactive molecules in conjunction with psychotherapy as a unique potential treatment approach for patients with comorbid mood and alcohol/sedative use disorders. In this “Innovators & Ideas” section, we are excited to feature Dr Garel in our latest Genomic Press Interview. We are thrilled he took the time to answer our questions and share his valuable insights with our readers.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".