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 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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".